diff --git a/README.md b/README.md index ab231b51..c43de908 100644 --- a/README.md +++ b/README.md @@ -19,7 +19,7 @@ A paper introducing jMetalPy is available at: https://doi.org/10.1016/j.swevo.20 You can install the latest version of jMetalPy with `pip`, ```console -pip install jmetalpy +pip install jmetalpy # or "jmetalpy[distributed]" ```
Notes on installing with pip @@ -27,13 +27,13 @@ pip install jmetalpy jMetalPy includes features for parallel and distributed computing based on [pySpark](https://spark.apache.org/docs/latest/api/python/index.html) and [Dask](https://dask.org/). -These (extra) dependencies are automatically installed when running `pip`: +These (extra) dependencies are *not* automatically installed when running `pip`, which only comprises the core functionality of the framework (enough for most users): ```console pip install jmetalpy ``` -But you can also install the core functionality of the framework (which is often enough for most users): +This is the equivalent of running: ```console pip install "jmetalpy[core]" diff --git a/docs/_modules/index.html b/docs/_modules/index.html index 922a8940..97c8bd93 100644 --- a/docs/_modules/index.html +++ b/docs/_modules/index.html @@ -24,7 +24,6 @@ - @@ -73,8 +72,6 @@

Table Of Contents

  • Single-objective algorithms
  • Operators
  • Problems
    • -
  • Contributing
    • -
  • About
    • @@ -115,7 +112,6 @@

    All modules for which code is available

  • jmetal.algorithm.multiobjective.mocell
  • jmetal.algorithm.multiobjective.moead
  • jmetal.algorithm.multiobjective.nsgaii
    • -
  • jmetal.algorithm.multiobjective.nsgaiii
  • jmetal.algorithm.multiobjective.omopso
  • jmetal.algorithm.multiobjective.smpso
  • jmetal.algorithm.multiobjective.spea2
    • @@ -125,8 +121,10 @@

    All modules for which code is available

  • jmetal.algorithm.singleobjective.simulated_annealing
  • jmetal.core.observer
  • jmetal.core.problem
    • +
  • jmetal.lab.experiment
  • jmetal.lab.statistical_test.apv_procedures
  • jmetal.lab.statistical_test.bayesian
    • +
  • jmetal.lab.statistical_test.critical_distance
  • jmetal.lab.statistical_test.functions
  • jmetal.lab.visualization.chord_plot
  • jmetal.lab.visualization.interactive
    • diff --git a/docs/_modules/jmetal/algorithm/multiobjective/gde3.html b/docs/_modules/jmetal/algorithm/multiobjective/gde3.html index 5e6fbd7f..c7718d38 100644 --- a/docs/_modules/jmetal/algorithm/multiobjective/gde3.html +++ b/docs/_modules/jmetal/algorithm/multiobjective/gde3.html @@ -24,7 +24,6 @@ - @@ -74,8 +73,6 @@

    Table Of Contents

  • Single-objective algorithms
  • Operators
  • Problems
    • -
  • Contributing
    • -
  • About
    • @@ -112,18 +109,18 @@

    Table Of Contents

    Source code for jmetal.algorithm.multiobjective.gde3

    -from typing import TypeVar, List
-
-from jmetal.config import store
-from jmetal.core.algorithm import EvolutionaryAlgorithm, DynamicAlgorithm
-from jmetal.core.problem import Problem, DynamicProblem
-from jmetal.core.solution import FloatSolution
-from jmetal.operator import DifferentialEvolutionCrossover, RankingAndCrowdingDistanceSelection
-from jmetal.operator.selection import DifferentialEvolutionSelection
-from jmetal.util.comparator import Comparator, DominanceComparator
-from jmetal.util.evaluator import Evaluator
-from jmetal.util.generator import Generator
-from jmetal.util.termination_criterion import TerminationCriterion
+from typing import TypeVar, List
+
+from jmetal.config import store
+from jmetal.core.algorithm import EvolutionaryAlgorithm, DynamicAlgorithm
+from jmetal.core.problem import Problem, DynamicProblem
+from jmetal.core.solution import FloatSolution
+from jmetal.operator import DifferentialEvolutionCrossover, RankingAndCrowdingDistanceSelection
+from jmetal.operator.selection import DifferentialEvolutionSelection
+from jmetal.util.comparator import Comparator, DominanceComparator
+from jmetal.util.evaluator import Evaluator
+from jmetal.util.generator import Generator
+from jmetal.util.termination_criterion import TerminationCriterion
 
 S = TypeVar('S')
 R = List[S]
@@ -131,7 +128,7 @@ 
    Source code for jmetal.algorithm.multiobjective.gde3
    [docs]class GDE3(EvolutionaryAlgorithm[FloatSolution, FloatSolution]):
 
-    def __init__(self,
+    def __init__(self,
                  problem: Problem,
                  population_size: int,
                  cr: float,
@@ -215,7 +212,7 @@ 
    Source code for jmetal.algorithm.multiobjective.gde3
    [docs]class DynamicGDE3(GDE3, DynamicAlgorithm):
 
-    def __init__(self,
+    def __init__(self,
                  problem: DynamicProblem,
                  population_size: int,
                  cr: float,
diff --git a/docs/_modules/jmetal/algorithm/multiobjective/hype.html b/docs/_modules/jmetal/algorithm/multiobjective/hype.html
index 69cc2ff6..32d9c76d 100644
--- a/docs/_modules/jmetal/algorithm/multiobjective/hype.html
+++ b/docs/_modules/jmetal/algorithm/multiobjective/hype.html
@@ -24,7 +24,6 @@
     
     
     
-    
     
     
   
@@ -74,8 +73,6 @@ 
    Table Of Contents
    
 
  • Single-objective algorithms
    • 
 
  • Operators
    • 
 
  • Problems
    • 
-
  • Contributing
    • 
-
  • About
    • 
 
 
     
@@ -112,20 +109,20 @@ 
    Table Of Contents
    
           
    
             
   
    Source code for jmetal.algorithm.multiobjective.hype
    -from typing import TypeVar, List
-
-from jmetal.algorithm.singleobjective.genetic_algorithm import GeneticAlgorithm
-from jmetal.config import store
-from jmetal.core.operator import Mutation, Crossover
-from jmetal.core.problem import Problem
-from jmetal.core.solution import Solution
-from jmetal.operator import BinaryTournamentSelection
-from jmetal.operator.selection import RankingAndFitnessSelection
-from jmetal.util.comparator import Comparator
-from jmetal.util.comparator import SolutionAttributeComparator
-from jmetal.util.evaluator import Evaluator
-from jmetal.util.generator import Generator
-from jmetal.util.termination_criterion import TerminationCriterion
+from typing import TypeVar, List
+
+from jmetal.algorithm.singleobjective.genetic_algorithm import GeneticAlgorithm
+from jmetal.config import store
+from jmetal.core.operator import Mutation, Crossover
+from jmetal.core.problem import Problem
+from jmetal.core.solution import Solution
+from jmetal.operator import BinaryTournamentSelection
+from jmetal.operator.selection import RankingAndFitnessSelection
+from jmetal.util.comparator import Comparator
+from jmetal.util.comparator import SolutionAttributeComparator
+from jmetal.util.evaluator import Evaluator
+from jmetal.util.generator import Generator
+from jmetal.util.termination_criterion import TerminationCriterion
 
 S = TypeVar('S')
 R = TypeVar('R')
@@ -133,7 +130,7 @@ 
    Source code for jmetal.algorithm.multiobjective.hype
    [docs]class HYPE(GeneticAlgorithm[S, R]):
 
-    def __init__(self,
+    def __init__(self,
                  problem: Problem,
                  reference_point: Solution,
                  population_size: int,
diff --git a/docs/_modules/jmetal/algorithm/multiobjective/ibea.html b/docs/_modules/jmetal/algorithm/multiobjective/ibea.html
index 4f2de820..2abadca6 100644
--- a/docs/_modules/jmetal/algorithm/multiobjective/ibea.html
+++ b/docs/_modules/jmetal/algorithm/multiobjective/ibea.html
@@ -24,7 +24,6 @@
     
     
     
-    
     
     
   
@@ -74,8 +73,6 @@ 
    Table Of Contents
    
 
  • Single-objective algorithms
    • 
 
  • Operators
    • 
 
  • Problems
    • 
-
  • Contributing
    • 
-
  • About
    • 
 
 
     
@@ -112,20 +109,20 @@ 
    Table Of Contents
    
           
    
             
   
    Source code for jmetal.algorithm.multiobjective.ibea
    -from typing import TypeVar, List
+from typing import TypeVar, List
 
 import numpy as np
 
-from jmetal.algorithm.singleobjective.genetic_algorithm import GeneticAlgorithm
-from jmetal.config import store
-from jmetal.core.operator import Mutation, Crossover
-from jmetal.core.problem import Problem
-from jmetal.core.quality_indicator import EpsilonIndicator
-from jmetal.operator import BinaryTournamentSelection
-from jmetal.util.comparator import SolutionAttributeComparator
-from jmetal.util.evaluator import Evaluator
-from jmetal.util.generator import Generator
-from jmetal.util.termination_criterion import TerminationCriterion
+from jmetal.algorithm.singleobjective.genetic_algorithm import GeneticAlgorithm
+from jmetal.config import store
+from jmetal.core.operator import Mutation, Crossover
+from jmetal.core.problem import Problem
+from jmetal.core.quality_indicator import EpsilonIndicator
+from jmetal.operator import BinaryTournamentSelection
+from jmetal.util.comparator import SolutionAttributeComparator
+from jmetal.util.evaluator import Evaluator
+from jmetal.util.generator import Generator
+from jmetal.util.termination_criterion import TerminationCriterion
 
 S = TypeVar('S')
 R = TypeVar('R')
@@ -133,7 +130,7 @@ 
    Source code for jmetal.algorithm.multiobjective.ibea
    [docs]class IBEA(GeneticAlgorithm[S, R]):
 
-    def __init__(self,
+    def __init__(self,
                  problem: Problem,
                  population_size: int,
                  offspring_population_size: int,
diff --git a/docs/_modules/jmetal/algorithm/multiobjective/mocell.html b/docs/_modules/jmetal/algorithm/multiobjective/mocell.html
index 9036703a..7f77fea6 100644
--- a/docs/_modules/jmetal/algorithm/multiobjective/mocell.html
+++ b/docs/_modules/jmetal/algorithm/multiobjective/mocell.html
@@ -24,7 +24,6 @@
     
     
     
-    
     
     
   
@@ -74,8 +73,6 @@ 
    Table Of Contents
    
 
  • Single-objective algorithms
    • 
 
  • Operators
    • 
 
  • Problems
    • 
-
  • Contributing
    • 
-
  • About
    • 
 
 
     
@@ -113,22 +110,22 @@ 
    Table Of Contents
    
             
   
    Source code for jmetal.algorithm.multiobjective.mocell
     import copy
-from functools import cmp_to_key
-from typing import TypeVar, List
-
-from jmetal.algorithm.singleobjective.genetic_algorithm import GeneticAlgorithm
-from jmetal.config import store
-from jmetal.core.operator import Mutation, Crossover, Selection
-from jmetal.core.problem import Problem
-from jmetal.operator import BinaryTournamentSelection
-from jmetal.util.archive import BoundedArchive
-from jmetal.util.density_estimator import CrowdingDistance, DensityEstimator
-from jmetal.util.evaluator import Evaluator
-from jmetal.util.generator import Generator
-from jmetal.util.neighborhood import Neighborhood
-from jmetal.util.ranking import FastNonDominatedRanking, Ranking
-from jmetal.util.comparator import Comparator, MultiComparator
-from jmetal.util.termination_criterion import TerminationCriterion
+from functools import cmp_to_key
+from typing import TypeVar, List
+
+from jmetal.algorithm.singleobjective.genetic_algorithm import GeneticAlgorithm
+from jmetal.config import store
+from jmetal.core.operator import Mutation, Crossover, Selection
+from jmetal.core.problem import Problem
+from jmetal.operator import BinaryTournamentSelection
+from jmetal.util.archive import BoundedArchive
+from jmetal.util.density_estimator import CrowdingDistance, DensityEstimator
+from jmetal.util.evaluator import Evaluator
+from jmetal.util.generator import Generator
+from jmetal.util.neighborhood import Neighborhood
+from jmetal.util.ranking import FastNonDominatedRanking, Ranking
+from jmetal.util.comparator import Comparator, MultiComparator
+from jmetal.util.termination_criterion import TerminationCriterion
 
 S = TypeVar('S')
 R = TypeVar('R')
@@ -143,7 +140,7 @@ 
    Source code for jmetal.algorithm.multiobjective.mocell
    [docs]class MOCell(GeneticAlgorithm[S, R]):
 
-    def __init__(self,
+    def __init__(self,
                  problem: Problem,
                  population_size: int,
                  neighborhood: Neighborhood,
diff --git a/docs/_modules/jmetal/algorithm/multiobjective/moead.html b/docs/_modules/jmetal/algorithm/multiobjective/moead.html
index ccbc42ad..8b4239f0 100644
--- a/docs/_modules/jmetal/algorithm/multiobjective/moead.html
+++ b/docs/_modules/jmetal/algorithm/multiobjective/moead.html
@@ -24,7 +24,6 @@
     
     
     
-    
     
     
   
@@ -74,8 +73,6 @@ 
    Table Of Contents
    
 
  • Single-objective algorithms
    • 
 
  • Operators
    • 
 
  • Problems
    • 
-
  • Contributing
    • 
-
  • About
    • 
 
 
     
@@ -114,24 +111,24 @@ 
    Table Of Contents
    
   
    Source code for jmetal.algorithm.multiobjective.moead
     import copy
 import random
-from math import ceil
-from typing import TypeVar, List, Generator
+from math import ceil
+from typing import TypeVar, List, Generator
 
 import numpy as np
 
-from jmetal.algorithm.singleobjective.genetic_algorithm import GeneticAlgorithm
-from jmetal.config import store
-from jmetal.core.operator import Mutation
-from jmetal.core.problem import Problem
-from jmetal.operator import DifferentialEvolutionCrossover, NaryRandomSolutionSelection
-from jmetal.util.aggregative_function import AggregativeFunction
-from jmetal.util.constraint_handling import feasibility_ratio, \
+from jmetal.algorithm.singleobjective.genetic_algorithm import GeneticAlgorithm
+from jmetal.config import store
+from jmetal.core.operator import Mutation
+from jmetal.core.problem import Problem
+from jmetal.operator import DifferentialEvolutionCrossover, NaryRandomSolutionSelection
+from jmetal.util.aggregative_function import AggregativeFunction
+from jmetal.util.constraint_handling import feasibility_ratio, \
     overall_constraint_violation_degree, is_feasible
-from jmetal.util.density_estimator import CrowdingDistance
-from jmetal.util.evaluator import Evaluator
-from jmetal.util.neighborhood import WeightVectorNeighborhood
-from jmetal.util.ranking import FastNonDominatedRanking
-from jmetal.util.termination_criterion import TerminationCriterion, StoppingByEvaluations
+from jmetal.util.density_estimator import CrowdingDistance
+from jmetal.util.evaluator import Evaluator
+from jmetal.util.neighborhood import WeightVectorNeighborhood
+from jmetal.util.ranking import FastNonDominatedRanking
+from jmetal.util.termination_criterion import TerminationCriterion, StoppingByEvaluations
 
 S = TypeVar('S')
 R = List[S]
@@ -139,7 +136,7 @@ 
    Source code for jmetal.algorithm.multiobjective.moead
    [docs]class MOEAD(GeneticAlgorithm):
 
-    def __init__(self,
+    def __init__(self,
                  problem: Problem,
                  population_size: int,
                  mutation: Mutation,
@@ -268,7 +265,7 @@ 
    Source code for jmetal.algorithm.multiobjective.moead
    class MOEAD_DRA(MOEAD):
-    def __init__(self, problem, population_size, mutation, crossover, aggregative_function,
+    def __init__(self, problem, population_size, mutation, crossover, aggregative_function,
                  neighbourhood_selection_probability, max_number_of_replaced_solutions, neighbor_size,
                  weight_files_path, termination_criterion=store.default_termination_criteria,
                  population_generator=store.default_generator, population_evaluator=store.default_evaluator):
@@ -365,7 +362,7 @@ 
    Source code for jmetal.algorithm.multiobjective.moead
    class MOEADIEpsilon(MOEAD):
-    def __init__(self,
+    def __init__(self,
                  problem: Problem,
                  population_size: int,
                  mutation: Mutation,
@@ -506,7 +503,7 @@ 
    Source code for jmetal.algorithm.multiobjective.moead
    class Permutation:
 
-    def __init__(self, length: int):
+    def __init__(self, length: int):
         self.counter = 0
         self.length = length
         self.permutation = np.random.permutation(length)
diff --git a/docs/_modules/jmetal/algorithm/multiobjective/nsgaii.html b/docs/_modules/jmetal/algorithm/multiobjective/nsgaii.html
index b1e46e68..7b3e7647 100644
--- a/docs/_modules/jmetal/algorithm/multiobjective/nsgaii.html
+++ b/docs/_modules/jmetal/algorithm/multiobjective/nsgaii.html
@@ -24,7 +24,6 @@
     
     
     
-    
     
     
   
@@ -74,8 +73,6 @@ 
    Table Of Contents
    
 
  • Single-objective algorithms
    • 
 
  • Operators
    • 
 
  • Problems
    • 
-
  • Contributing
    • 
-
  • About
    • 
 
 
     
@@ -113,26 +110,26 @@ 
    Table Of Contents
    
             
   
    Source code for jmetal.algorithm.multiobjective.nsgaii
     import time
-from typing import TypeVar, List, Generator
+from typing import TypeVar, List, Generator
 
 try:
     import dask
-    from distributed import as_completed, Client
+    from distributed import as_completed, Client
 except ImportError:
     pass
 
-from jmetal.algorithm.singleobjective.genetic_algorithm import GeneticAlgorithm
-from jmetal.config import store
-from jmetal.core.algorithm import DynamicAlgorithm, Algorithm
-from jmetal.core.operator import Mutation, Crossover, Selection
-from jmetal.core.problem import Problem, DynamicProblem
-from jmetal.operator import BinaryTournamentSelection
-from jmetal.util.density_estimator import CrowdingDistance
-from jmetal.util.evaluator import Evaluator
-from jmetal.util.ranking import FastNonDominatedRanking
-from jmetal.util.replacement import RankingAndDensityEstimatorReplacement, RemovalPolicyType
-from jmetal.util.comparator import DominanceComparator, Comparator, MultiComparator
-from jmetal.util.termination_criterion import TerminationCriterion
+from jmetal.algorithm.singleobjective.genetic_algorithm import GeneticAlgorithm
+from jmetal.config import store
+from jmetal.core.algorithm import DynamicAlgorithm, Algorithm
+from jmetal.core.operator import Mutation, Crossover, Selection
+from jmetal.core.problem import Problem, DynamicProblem
+from jmetal.operator import BinaryTournamentSelection
+from jmetal.util.density_estimator import CrowdingDistance
+from jmetal.util.evaluator import Evaluator
+from jmetal.util.ranking import FastNonDominatedRanking
+from jmetal.util.replacement import RankingAndDensityEstimatorReplacement, RemovalPolicyType
+from jmetal.util.comparator import DominanceComparator, Comparator, MultiComparator
+from jmetal.util.termination_criterion import TerminationCriterion
 
 S = TypeVar('S')
 R = TypeVar('R')
@@ -148,7 +145,7 @@ 
    Source code for jmetal.algorithm.multiobjective.nsgaii
    [docs]class NSGAII(GeneticAlgorithm[S, R]):
 
-    def __init__(self,
+    def __init__(self,
                  problem: Problem,
                  population_size: int,
                  offspring_population_size: int,
@@ -218,7 +215,7 @@ 
    Source code for jmetal.algorithm.multiobjective.nsgaii
    [docs]class DynamicNSGAII(NSGAII[S, R], DynamicAlgorithm):
 
-    def __init__(self,
+    def __init__(self,
                  problem: DynamicProblem[S],
                  population_size: int,
                  offspring_population_size: int,
@@ -273,7 +270,7 @@ 
    Source code for jmetal.algorithm.multiobjective.nsgaii
    [docs]class DistributedNSGAII(Algorithm[S, R]):
 
-    def __init__(self,
+    def __init__(self,
                  problem: Problem,
                  population_size: int,
                  mutation: Mutation,
diff --git a/docs/_modules/jmetal/algorithm/multiobjective/omopso.html b/docs/_modules/jmetal/algorithm/multiobjective/omopso.html
index 0dab4e7b..3072244d 100644
--- a/docs/_modules/jmetal/algorithm/multiobjective/omopso.html
+++ b/docs/_modules/jmetal/algorithm/multiobjective/omopso.html
@@ -24,7 +24,6 @@
     
     
     
-    
     
     
   
@@ -74,8 +73,6 @@ 
    Table Of Contents
    
 
  • Single-objective algorithms
    • 
 
  • Operators
    • 
 
  • Problems
    • 
-
  • Contributing
    • 
-
  • About
    • 
 
 
     
@@ -113,23 +110,23 @@ 
    Table Of Contents
    
             
   
    Source code for jmetal.algorithm.multiobjective.omopso
     import random
-from copy import copy
-from math import sqrt
-from typing import TypeVar, List, Optional
+from copy import copy
+from math import sqrt
+from typing import TypeVar, List, Optional
 
 import numpy
 
-from jmetal.config import store
-from jmetal.core.algorithm import ParticleSwarmOptimization
-from jmetal.core.problem import FloatProblem
-from jmetal.core.solution import FloatSolution
-from jmetal.operator import UniformMutation
-from jmetal.operator.mutation import NonUniformMutation
-from jmetal.util.archive import BoundedArchive, NonDominatedSolutionsArchive
-from jmetal.util.comparator import DominanceComparator, EpsilonDominanceComparator
-from jmetal.util.evaluator import Evaluator
-from jmetal.util.generator import Generator
-from jmetal.util.termination_criterion import TerminationCriterion
+from jmetal.config import store
+from jmetal.core.algorithm import ParticleSwarmOptimization
+from jmetal.core.problem import FloatProblem
+from jmetal.core.solution import FloatSolution
+from jmetal.operator import UniformMutation
+from jmetal.operator.mutation import NonUniformMutation
+from jmetal.util.archive import BoundedArchive, NonDominatedSolutionsArchive
+from jmetal.util.comparator import DominanceComparator, EpsilonDominanceComparator
+from jmetal.util.evaluator import Evaluator
+from jmetal.util.generator import Generator
+from jmetal.util.termination_criterion import TerminationCriterion
 
 R = TypeVar('R')
 
@@ -144,7 +141,7 @@ 
    Source code for jmetal.algorithm.multiobjective.omopso
    [docs]class OMOPSO(ParticleSwarmOptimization):
 
-    def __init__(self,
+    def __init__(self,
                  problem: FloatProblem,
                  swarm_size: int,
                  uniform_mutation: UniformMutation,
diff --git a/docs/_modules/jmetal/algorithm/multiobjective/smpso.html b/docs/_modules/jmetal/algorithm/multiobjective/smpso.html
index c97e96e2..34e469af 100644
--- a/docs/_modules/jmetal/algorithm/multiobjective/smpso.html
+++ b/docs/_modules/jmetal/algorithm/multiobjective/smpso.html
@@ -24,7 +24,6 @@
     
     
     
-    
     
     
   
@@ -74,8 +73,6 @@ 
    Table Of Contents
    
 
  • Single-objective algorithms
    • 
 
  • Operators
    • 
 
  • Problems
    • 
-
  • Contributing
    • 
-
  • About
    • 
 
 
     
@@ -114,22 +111,22 @@ 
    Table Of Contents
    
   
    Source code for jmetal.algorithm.multiobjective.smpso
     import random
 import threading
-from copy import copy
-from math import sqrt
-from typing import TypeVar, List, Optional
+from copy import copy
+from math import sqrt
+from typing import TypeVar, List, Optional
 
 import numpy
 
-from jmetal.config import store
-from jmetal.core.algorithm import ParticleSwarmOptimization, DynamicAlgorithm
-from jmetal.core.operator import Mutation
-from jmetal.core.problem import FloatProblem, DynamicProblem
-from jmetal.core.solution import FloatSolution
-from jmetal.util.archive import BoundedArchive, ArchiveWithReferencePoint
-from jmetal.util.comparator import DominanceComparator
-from jmetal.util.evaluator import Evaluator
-from jmetal.util.generator import Generator
-from jmetal.util.termination_criterion import TerminationCriterion
+from jmetal.config import store
+from jmetal.core.algorithm import ParticleSwarmOptimization, DynamicAlgorithm
+from jmetal.core.operator import Mutation
+from jmetal.core.problem import FloatProblem, DynamicProblem
+from jmetal.core.solution import FloatSolution
+from jmetal.util.archive import BoundedArchive, ArchiveWithReferencePoint
+from jmetal.util.comparator import DominanceComparator
+from jmetal.util.evaluator import Evaluator
+from jmetal.util.generator import Generator
+from jmetal.util.termination_criterion import TerminationCriterion
 
 R = TypeVar('R')
 
@@ -144,7 +141,7 @@ 
    Source code for jmetal.algorithm.multiobjective.smpso
    [docs]class SMPSO(ParticleSwarmOptimization):
 
-    def __init__(self,
+    def __init__(self,
                  problem: FloatProblem,
                  swarm_size: int,
                  mutation: Mutation,
@@ -334,7 +331,7 @@ 
    Source code for jmetal.algorithm.multiobjective.smpso
    [docs]class DynamicSMPSO(SMPSO, DynamicAlgorithm):
 
-    def __init__(self,
+    def __init__(self,
                  problem: DynamicProblem[FloatSolution],
                  swarm_size: int,
                  mutation: Mutation,
@@ -388,7 +385,7 @@ 
    Source code for jmetal.algorithm.multiobjective.smpso
    [docs]class SMPSORP(SMPSO):
 
-    def __init__(self,
+    def __init__(self,
                  problem: FloatProblem,
                  swarm_size: int,
                  mutation: Mutation,
@@ -505,7 +502,7 @@ 
    Source code for jmetal.algorithm.multiobjective.smpso
    number_of_objectives = algorithm.problem.number_of_objectives
 
     while True:
-        print(f'Enter {number_of_reference_points}-points of dimension {number_of_objectives}: ')
+        print(f'Enter {number_of_reference_points}-points of dimension {number_of_objectives}: ')
         read = [float(x) for x in input().split()]
 
         # Update reference points
diff --git a/docs/_modules/jmetal/algorithm/multiobjective/spea2.html b/docs/_modules/jmetal/algorithm/multiobjective/spea2.html
index 1adcbf4b..f7257bdb 100644
--- a/docs/_modules/jmetal/algorithm/multiobjective/spea2.html
+++ b/docs/_modules/jmetal/algorithm/multiobjective/spea2.html
@@ -24,7 +24,6 @@
     
     
     
-    
     
     
   
@@ -74,8 +73,6 @@ 
    Table Of Contents
    
 
  • Single-objective algorithms
    • 
 
  • Operators
    • 
 
  • Problems
    • 
-
  • Contributing
    • 
-
  • About
    • 
 
 
     
@@ -112,20 +109,20 @@ 
    Table Of Contents
    
           
    
             
   
    Source code for jmetal.algorithm.multiobjective.spea2
    -from typing import TypeVar, List
-
-from jmetal.algorithm.singleobjective.genetic_algorithm import GeneticAlgorithm
-from jmetal.config import store
-from jmetal.core.operator import Mutation, Crossover
-from jmetal.core.problem import Problem
-from jmetal.operator import BinaryTournamentSelection
-from jmetal.util.density_estimator import KNearestNeighborDensityEstimator
-from jmetal.util.evaluator import Evaluator
-from jmetal.util.generator import Generator
-from jmetal.util.ranking import StrengthRanking
-from jmetal.util.replacement import RankingAndDensityEstimatorReplacement, RemovalPolicyType
-from jmetal.util.comparator import Comparator, MultiComparator
-from jmetal.util.termination_criterion import TerminationCriterion
+from typing import TypeVar, List
+
+from jmetal.algorithm.singleobjective.genetic_algorithm import GeneticAlgorithm
+from jmetal.config import store
+from jmetal.core.operator import Mutation, Crossover
+from jmetal.core.problem import Problem
+from jmetal.operator import BinaryTournamentSelection
+from jmetal.util.density_estimator import KNearestNeighborDensityEstimator
+from jmetal.util.evaluator import Evaluator
+from jmetal.util.generator import Generator
+from jmetal.util.ranking import StrengthRanking
+from jmetal.util.replacement import RankingAndDensityEstimatorReplacement, RemovalPolicyType
+from jmetal.util.comparator import Comparator, MultiComparator
+from jmetal.util.termination_criterion import TerminationCriterion
 
 S = TypeVar('S')
 R = TypeVar('R')
@@ -148,7 +145,7 @@ 
    Source code for jmetal.algorithm.multiobjective.spea2
    [docs]class SPEA2(GeneticAlgorithm[S, R]):
 
-    def __init__(self,
+    def __init__(self,
                  problem: Problem,
                  population_size: int,
                  offspring_population_size: int,
diff --git a/docs/_modules/jmetal/algorithm/singleobjective/evolution_strategy.html b/docs/_modules/jmetal/algorithm/singleobjective/evolution_strategy.html
index 0b0dcf9c..a38b68fd 100644
--- a/docs/_modules/jmetal/algorithm/singleobjective/evolution_strategy.html
+++ b/docs/_modules/jmetal/algorithm/singleobjective/evolution_strategy.html
@@ -24,7 +24,6 @@
     
     
     
-    
     
     
   
@@ -74,8 +73,6 @@ 
    Table Of Contents
    
 
  • Single-objective algorithms
    • 
 
  • Operators
    • 
 
  • Problems
    • 
-
  • Contributing
    • 
-
  • About
    • 
 
 
     
@@ -112,15 +109,15 @@ 
    Table Of Contents
    
           
    
             
   
    Source code for jmetal.algorithm.singleobjective.evolution_strategy
    -from copy import copy
-from typing import TypeVar, List
+from copy import copy
+from typing import TypeVar, List
 
-from jmetal.core.algorithm import EvolutionaryAlgorithm
-from jmetal.core.operator import Mutation
-from jmetal.core.problem import Problem
-from jmetal.util.evaluator import Evaluator, SequentialEvaluator
-from jmetal.util.generator import Generator, RandomGenerator
-from jmetal.util.termination_criterion import TerminationCriterion
+from jmetal.core.algorithm import EvolutionaryAlgorithm
+from jmetal.core.operator import Mutation
+from jmetal.core.problem import Problem
+from jmetal.util.evaluator import Evaluator, SequentialEvaluator
+from jmetal.util.generator import Generator, RandomGenerator
+from jmetal.util.termination_criterion import TerminationCriterion
 
 S = TypeVar('S')
 R = TypeVar('R')
@@ -136,7 +133,7 @@ 
    Source code for jmetal.algorithm.singleobjective.evolution_strategy
    [docs]class EvolutionStrategy(EvolutionaryAlgorithm[S, R]):
 
-    def __init__(self,
+    def __init__(self,
                  problem: Problem,
                  mu: int,
                  lambda_: int,
diff --git a/docs/_modules/jmetal/algorithm/singleobjective/genetic_algorithm.html b/docs/_modules/jmetal/algorithm/singleobjective/genetic_algorithm.html
index 2c38b0c3..7ff22c9d 100644
--- a/docs/_modules/jmetal/algorithm/singleobjective/genetic_algorithm.html
+++ b/docs/_modules/jmetal/algorithm/singleobjective/genetic_algorithm.html
@@ -24,7 +24,6 @@
     
     
     
-    
     
     
   
@@ -74,8 +73,6 @@ 
    Table Of Contents
    
 
  • Single-objective algorithms
    • 
 
  • Operators
    • 
 
  • Problems
    • 
-
  • Contributing
    • 
-
  • About
    • 
 
 
     
@@ -112,15 +109,15 @@ 
    Table Of Contents
    
           
    
             
   
    Source code for jmetal.algorithm.singleobjective.genetic_algorithm
    -from typing import TypeVar, List
+from typing import TypeVar, List
 
-from jmetal.config import store
-from jmetal.core.algorithm import EvolutionaryAlgorithm
-from jmetal.core.operator import Mutation, Crossover, Selection
-from jmetal.core.problem import Problem
-from jmetal.util.evaluator import Evaluator
-from jmetal.util.generator import Generator
-from jmetal.util.termination_criterion import TerminationCriterion
+from jmetal.config import store
+from jmetal.core.algorithm import EvolutionaryAlgorithm
+from jmetal.core.operator import Mutation, Crossover, Selection
+from jmetal.core.problem import Problem
+from jmetal.util.evaluator import Evaluator
+from jmetal.util.generator import Generator
+from jmetal.util.termination_criterion import TerminationCriterion
 
 S = TypeVar('S')
 R = TypeVar('R')
@@ -135,7 +132,7 @@ 
    Source code for jmetal.algorithm.singleobjective.genetic_algorithm
    [docs]class GeneticAlgorithm(EvolutionaryAlgorithm[S, R]):
 
-    def __init__(self,
+    def __init__(self,
                  problem: Problem,
                  population_size: int,
                  offspring_population_size: int,
diff --git a/docs/_modules/jmetal/algorithm/singleobjective/local_search.html b/docs/_modules/jmetal/algorithm/singleobjective/local_search.html
index 44203514..76de77d3 100644
--- a/docs/_modules/jmetal/algorithm/singleobjective/local_search.html
+++ b/docs/_modules/jmetal/algorithm/singleobjective/local_search.html
@@ -24,7 +24,6 @@
     
     
     
-    
     
     
   
@@ -74,8 +73,6 @@ 
    Table Of Contents
    
 
  • Single-objective algorithms
    • 
 
  • Operators
    • 
 
  • Problems
    • 
-
  • Contributing
    • 
-
  • About
    • 
 
 
     
@@ -116,15 +113,15 @@ 
    Source code for jmetal.algorithm.singleobjective.local_search
    import random
 import threading
 import time
-from typing import TypeVar, List
+from typing import TypeVar, List
 
-from jmetal.config import store
-from jmetal.core.algorithm import Algorithm
-from jmetal.core.operator import Mutation
-from jmetal.core.problem import Problem
-from jmetal.core.solution import Solution
-from jmetal.util.comparator import Comparator
-from jmetal.util.termination_criterion import TerminationCriterion
+from jmetal.config import store
+from jmetal.core.algorithm import Algorithm
+from jmetal.core.operator import Mutation
+from jmetal.core.problem import Problem
+from jmetal.core.solution import Solution
+from jmetal.util.comparator import Comparator
+from jmetal.util.termination_criterion import TerminationCriterion
 
 S = TypeVar('S')
 R = TypeVar('R')
@@ -140,7 +137,7 @@ 
    Source code for jmetal.algorithm.singleobjective.local_search
    [docs]class LocalSearch(Algorithm[S, R], threading.Thread):
 
-    def __init__(self,
+    def __init__(self,
                  problem: Problem[S],
                  mutation: Mutation,
                  termination_criterion: TerminationCriterion = store.default_termination_criteria,
diff --git a/docs/_modules/jmetal/algorithm/singleobjective/simulated_annealing.html b/docs/_modules/jmetal/algorithm/singleobjective/simulated_annealing.html
index 6e8d2c57..6fabb7ae 100644
--- a/docs/_modules/jmetal/algorithm/singleobjective/simulated_annealing.html
+++ b/docs/_modules/jmetal/algorithm/singleobjective/simulated_annealing.html
@@ -24,7 +24,6 @@
     
     
     
-    
     
     
   
@@ -74,8 +73,6 @@ 
    Table Of Contents
    
 
  • Single-objective algorithms
    • 
 
  • Operators
    • 
 
  • Problems
    • 
-
  • Contributing
    • 
-
  • About
    • 
 
 
     
@@ -116,15 +113,15 @@ 
    Source code for jmetal.algorithm.singleobjective.simulated_annealing
    import random
 import threading
 import time
-from typing import TypeVar, List
+from typing import TypeVar, List
 
 import numpy
 
-from jmetal.core.algorithm import Algorithm
-from jmetal.core.operator import Mutation
-from jmetal.core.problem import Problem
-from jmetal.core.solution import Solution
-from jmetal.util.termination_criterion import TerminationCriterion
+from jmetal.core.algorithm import Algorithm
+from jmetal.core.operator import Mutation
+from jmetal.core.problem import Problem
+from jmetal.core.solution import Solution
+from jmetal.util.termination_criterion import TerminationCriterion
 
 S = TypeVar('S')
 R = TypeVar('R')
@@ -140,7 +137,7 @@ 
    Source code for jmetal.algorithm.singleobjective.simulated_annealing
    [docs]class SimulatedAnnealing(Algorithm[S, R], threading.Thread):
 
-    def __init__(self,
+    def __init__(self,
                  problem: Problem[S],
                  mutation: Mutation,
                  termination_criterion: TerminationCriterion):
diff --git a/docs/_modules/jmetal/core/observer.html b/docs/_modules/jmetal/core/observer.html
index 6b739ff4..0f3a67b4 100644
--- a/docs/_modules/jmetal/core/observer.html
+++ b/docs/_modules/jmetal/core/observer.html
@@ -24,7 +24,6 @@
     
     
     
-    
     
     
   
@@ -74,8 +73,6 @@ 
    Table Of Contents
    
 
  • Single-objective algorithms
    • 
 
  • Operators
    • 
 
  • Problems
    • 
-
  • Contributing
    • 
-
  • About
    • 
 
 
     
@@ -112,7 +109,7 @@ 
    Table Of Contents
    
           
    
             
   
    Source code for jmetal.core.observer
    -from abc import abstractmethod, ABC
+from abc import abstractmethod, ABC
 
 """
 .. module:: Observable
diff --git a/docs/_modules/jmetal/core/problem.html b/docs/_modules/jmetal/core/problem.html
index 66da5994..612b63b4 100644
--- a/docs/_modules/jmetal/core/problem.html
+++ b/docs/_modules/jmetal/core/problem.html
@@ -24,7 +24,6 @@
     
     
     
-    
     
     
   
@@ -74,8 +73,6 @@ 
    Table Of Contents
    
 
  • Single-objective algorithms
    • 
 
  • Operators
    • 
 
  • Problems
    • 
-
  • Contributing
    • 
-
  • About
    • 
 
 
     
@@ -114,11 +111,11 @@ 
    Table Of Contents
    
   
    Source code for jmetal.core.problem
     import logging
 import random
-from abc import ABC, abstractmethod
-from typing import Generic, TypeVar, List
+from abc import ABC, abstractmethod
+from typing import Generic, TypeVar, List
 
-from jmetal.core.observer import Observer
-from jmetal.core.solution import BinarySolution, FloatSolution, IntegerSolution, PermutationSolution
+from jmetal.core.observer import Observer
+from jmetal.core.solution import BinarySolution, FloatSolution, IntegerSolution, PermutationSolution
 
 LOGGER = logging.getLogger('jmetal')
 
@@ -131,7 +128,7 @@ 
    Source code for jmetal.core.problem
         MINIMIZE = -1
     MAXIMIZE = 1
 
-    def __init__(self):
+    def __init__(self):
         self.number_of_variables: int = 0
         self.number_of_objectives: int = 0
         self.number_of_constraints: int = 0
@@ -175,14 +172,14 @@ 
    Source code for jmetal.core.problem
     
    [docs]class BinaryProblem(Problem[BinarySolution], ABC):
     """ Class representing binary problems. """
 
-    def __init__(self):
+    def __init__(self):
         super(BinaryProblem, self).__init__()
    
 
 
 
    [docs]class FloatProblem(Problem[FloatSolution], ABC):
     """ Class representing float problems. """
 
-    def __init__(self):
+    def __init__(self):
         super(FloatProblem, self).__init__()
         self.lower_bound = []
         self.upper_bound = []
@@ -202,7 +199,7 @@ 
    Source code for jmetal.core.problem
     
    [docs]class IntegerProblem(Problem[IntegerSolution], ABC):
     """ Class representing integer problems. """
 
-    def __init__(self):
+    def __init__(self):
         super(IntegerProblem, self).__init__()
         self.lower_bound = None
         self.upper_bound = None
@@ -223,7 +220,7 @@ 
    Source code for jmetal.core.problem
     
    [docs]class PermutationProblem(Problem[PermutationSolution], ABC):
     """ Class representing permutation problems. """
 
-    def __init__(self):
+    def __init__(self):
         super(PermutationProblem, self).__init__()
    
 
 
@@ -254,7 +251,7 @@ 
    Source code for jmetal.core.problem
                 .add_constraint(c1)\
             .add_constraint(c2)
     """
-    def __init__(self):
+    def __init__(self):
         super(OnTheFlyFloatProblem, self).__init__()
         self.functions = []
         self.constraints = []
diff --git a/docs/_modules/jmetal/lab/experiment.html b/docs/_modules/jmetal/lab/experiment.html
index 8ee1bac0..6eb4eb19 100644
--- a/docs/_modules/jmetal/lab/experiment.html
+++ b/docs/_modules/jmetal/lab/experiment.html
@@ -10,10 +10,9 @@
   
   
   
-  
   
   
-    jmetal.lab.experiment — jMetalPy 1.5.1 documentation
+    jmetal.lab.experiment — jMetalPy 1.5.3 documentation
     
     
     
@@ -22,8 +21,9 @@
     
     
     
+    
     
-    
+    
     
     
   
@@ -39,7 +39,7 @@ 
    Navigation
    
         
  • 
           modules |
    • 
-        
  • jMetalPy 1.5.1 documentation »
    • 
+        
  • jMetalPy 1.5.3 documentation »
    • 
           
  • Module code »
    •  
       
     
    
@@ -50,7 +50,16 @@ 
    Navigation
    
       
    
   
    
     
    
+    ../../../index.html" class="text-logo">
+    jMetalPy 1.5.3 documentation
+    
    
+
+
+
    
+  
    
+    Python version of the jMetal framework
+  
    
+
    
 
    
   
    
     
    Table Of Contents
    
@@ -64,8 +73,6 @@ 
    Table Of Contents
    
 
  • Single-objective algorithms
    • 
 
  • Operators
    • 
 
  • Problems
    • 
-
  • Contributing
    • 
-
  • About
    • 
 
 
     
@@ -89,15 +96,15 @@ 
    Table Of Contents
    
   
    
         
    
           
-          
    
+          
    
+          
           
           
    
             
@@ -105,19 +112,19 @@ 
    Source code for jmetal.lab.experiment
     import io
 import logging
 import os
-from concurrent.futures import ProcessPoolExecutor
-from pathlib import Path
-from statistics import median
-from typing import List
+from concurrent.futures import ProcessPoolExecutor
+from pathlib import Path
+from statistics import median
+from typing import List
 
 import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
-from scipy.stats import mannwhitneyu
+from scipy.stats import mannwhitneyu
 
-from jmetal.core.algorithm import Algorithm
-from jmetal.core.quality_indicator import QualityIndicator
-from jmetal.util.solutions import print_function_values_to_file, print_variables_to_file, read_solutions
+from jmetal.core.algorithm import Algorithm
+from jmetal.core.quality_indicator import QualityIndicator
+from jmetal.util.solution import print_function_values_to_file, print_variables_to_file, read_solutions
 
 LOGGER = logging.getLogger('jmetal')
 
@@ -132,7 +139,7 @@ 
    Source code for jmetal.lab.experiment
     
 
    [docs]class Job:
 
-    def __init__(self, algorithm: Algorithm, algorithm_tag: str, problem_tag: str, run: int):
+    def __init__(self, algorithm: Algorithm, algorithm_tag: str, problem_tag: str, run: int):
         self.algorithm = algorithm
         self.algorithm_tag = algorithm_tag
         self.problem_tag = problem_tag
@@ -155,7 +162,7 @@ 
    Source code for jmetal.lab.experiment
     
 
    [docs]class Experiment:
 
-    def __init__(self, output_dir: str, jobs: List[Job], m_workers: int = 6):
+    def __init__(self, output_dir: str, jobs: List[Job], m_workers: int = 6):
         """ Run an experiment to execute a list of jobs.
 
         :param output_dir: Base directory where each job will save its results.
@@ -288,8 +295,8 @@ 
    Source code for jmetal.lab.experiment
                 ax.set_xticklabels(algorithms)
             ax.tick_params(labelsize=20)
 
-            plt.savefig('boxplot/boxplot-{}-{}.png'.format(pr, indicator_name), bbox_inches='tight')
-            plt.savefig('boxplot/boxplot-{}-{}.eps'.format(pr, indicator_name), bbox_inches='tight')
+            plt.savefig(os.path.join(output_dir, 'boxplot-{}-{}.png'.format(pr, indicator_name)), bbox_inches='tight')
+            plt.savefig(os.path.join(output_dir, 'boxplot-{}-{}.eps'.format(pr, indicator_name)), bbox_inches='tight')
             plt.close(fig)
 
 
@@ -663,7 +670,7 @@ 
    Navigation
    
         
  • 
           modules |
    • 
-        
  • jMetalPy 1.5.1 documentation »
    • 
+        
  • jMetalPy 1.5.3 documentation »
    • 
           
  • Module code »
    •  
       
     
    
diff --git a/docs/_modules/jmetal/lab/statistical_test/apv_procedures.html b/docs/_modules/jmetal/lab/statistical_test/apv_procedures.html
index 12fcaac4..3ccce2a7 100644
--- a/docs/_modules/jmetal/lab/statistical_test/apv_procedures.html
+++ b/docs/_modules/jmetal/lab/statistical_test/apv_procedures.html
@@ -24,7 +24,6 @@
     
     
     
-    
     
     
   
@@ -74,8 +73,6 @@ 
    Table Of Contents
    
 
  • Single-objective algorithms
    • 
 
  • Operators
    • 
 
  • Problems
    • 
-
  • Contributing
    • 
-
  • About
    • 
 
 
     
@@ -429,7 +426,7 @@ 
    Source code for jmetal.lab.statistical_test.apv_procedures
                Set of true hypotheses.
         """
 
-        from scipy.special import binom as binomial
+        from scipy.special import binom as binomial
 
         TrueHset = [0]
         if k > 1:
diff --git a/docs/_modules/jmetal/lab/statistical_test/bayesian.html b/docs/_modules/jmetal/lab/statistical_test/bayesian.html
index 3da97ae1..7c535914 100644
--- a/docs/_modules/jmetal/lab/statistical_test/bayesian.html
+++ b/docs/_modules/jmetal/lab/statistical_test/bayesian.html
@@ -24,7 +24,6 @@
     
     
     
-    
     
     
   
@@ -74,8 +73,6 @@ 
    Table Of Contents
    
 
  • Single-objective algorithms
    • 
 
  • Operators
    • 
 
  • Problems
    • 
-
  • Contributing
    • 
-
  • About
    • 
 
 
     
diff --git a/docs/_modules/jmetal/lab/statistical_test/critical_distance.html b/docs/_modules/jmetal/lab/statistical_test/critical_distance.html
index 4688bc0a..fbf2af5c 100644
--- a/docs/_modules/jmetal/lab/statistical_test/critical_distance.html
+++ b/docs/_modules/jmetal/lab/statistical_test/critical_distance.html
@@ -10,10 +10,9 @@
   
   
   
-  
   
   
-    jmetal.lab.statistical_test.critical_distance — jMetalPy 1.5.1 documentation
+    jmetal.lab.statistical_test.critical_distance — jMetalPy 1.5.3 documentation
     
     
     
@@ -22,8 +21,9 @@
     
     
     
+    
     
-    
+    
     
     
   
@@ -39,7 +39,7 @@ 
    Navigation
    
         
  • 
           modules |
    • 
-        
  • jMetalPy 1.5.1 documentation »
    • 
+        
  • jMetalPy 1.5.3 documentation »
    • 
           
  • Module code »
    •  
       
     
    
@@ -50,7 +50,16 @@ 
    Navigation
    
       
    
   
    
     
    
+    ../../../../index.html" class="text-logo">
+    jMetalPy 1.5.3 documentation
+    
    
+
+
+
    
+  
    
+    Python version of the jMetal framework
+  
    
+
    
 
    
   
    
     
    Table Of Contents
    
@@ -64,8 +73,6 @@ 
    Table Of Contents
    
 
  • Single-objective algorithms
    • 
 
  • Operators
    • 
 
  • Problems
    • 
-
  • Contributing
    • 
-
  • About
    • 
 
 
     
@@ -89,25 +96,25 @@ 
    Table Of Contents
    
   
    
         
    
           
-          
    
+          
    
+          
           
           
    
             
   
    Source code for jmetal.lab.statistical_test.critical_distance
     import numpy as np
 import pandas as pd
-from matplotlib import pyplot as plt
-from statsmodels.stats.libqsturng import qsturng
+from matplotlib import pyplot as plt
+from statsmodels.stats.libqsturng import qsturng
 
-from jmetal.lab.statistical_test.functions import ranks
+from jmetal.lab.statistical_test.functions import ranks
 
 
 
    [docs]def NemenyiCD(alpha: float, num_alg, num_dataset):
@@ -326,7 +333,7 @@ 
    Navigation
    
         
  • 
           modules |
    • 
-        
  • jMetalPy 1.5.1 documentation »
    • 
+        
  • jMetalPy 1.5.3 documentation »
    • 
           
  • Module code »
    •  
       
     
    
diff --git a/docs/_modules/jmetal/lab/statistical_test/functions.html b/docs/_modules/jmetal/lab/statistical_test/functions.html
index 98491255..550a8dc2 100644
--- a/docs/_modules/jmetal/lab/statistical_test/functions.html
+++ b/docs/_modules/jmetal/lab/statistical_test/functions.html
@@ -24,7 +24,6 @@
     
     
     
-    
     
     
   
@@ -74,8 +73,6 @@ 
    Table Of Contents
    
 
  • Single-objective algorithms
    • 
 
  • Operators
    • 
 
  • Problems
    • 
-
  • Contributing
    • 
-
  • About
    • 
 
 
     
@@ -112,9 +109,9 @@ 
    Table Of Contents
    
           
    
             
   
    Source code for jmetal.lab.statistical_test.functions
    -from scipy.stats import chi2, f, binom, norm
+from scipy.stats import chi2, f, binom, norm
 
-from jmetal.lab.statistical_test.apv_procedures import *
+from jmetal.lab.statistical_test.apv_procedures import *
 
 
 
    [docs]def ranks(data: np.array, descending=False):
diff --git a/docs/_modules/jmetal/lab/visualization/chord_plot.html b/docs/_modules/jmetal/lab/visualization/chord_plot.html
index b22fc6ba..4bd4aea9 100644
--- a/docs/_modules/jmetal/lab/visualization/chord_plot.html
+++ b/docs/_modules/jmetal/lab/visualization/chord_plot.html
@@ -24,7 +24,6 @@
     
     
     
-    
     
     
   
@@ -74,8 +73,6 @@ 
    Table Of Contents
    
 
  • Single-objective algorithms
    • 
 
  • Operators
    • 
 
  • Problems
    • 
-
  • Contributing
    • 
-
  • About
    • 
 
 
     
@@ -113,15 +110,15 @@ 
    Table Of Contents
    
             
   
    Source code for jmetal.lab.visualization.chord_plot
     import colorsys
-from typing import List
+from typing import List
 
 import numpy as np
-from matplotlib import patches
-from matplotlib import pyplot as plt
-from matplotlib.path import Path
-from tqdm import tqdm
+from matplotlib import patches
+from matplotlib import pyplot as plt
+from matplotlib.path import Path
+from tqdm import tqdm
 
-from jmetal.core.solution import FloatSolution
+from jmetal.core.solution import FloatSolution
 
 
 
    [docs]def polar_to_cartesian(r, theta):
diff --git a/docs/_modules/jmetal/lab/visualization/interactive.html b/docs/_modules/jmetal/lab/visualization/interactive.html
index 66958cb1..302b21f4 100644
--- a/docs/_modules/jmetal/lab/visualization/interactive.html
+++ b/docs/_modules/jmetal/lab/visualization/interactive.html
@@ -24,7 +24,6 @@
     
     
     
-    
     
     
   
@@ -74,8 +73,6 @@ 
    Table Of Contents
    
 
  • Single-objective algorithms
    • 
 
  • Operators
    • 
 
  • Problems
    • 
-
  • Contributing
    • 
-
  • About
    • 
 
 
     
@@ -113,14 +110,14 @@ 
    Table Of Contents
    
             
   
    Source code for jmetal.lab.visualization.interactive
     import logging
-from typing import TypeVar, List
+from typing import TypeVar, List
 
 import pandas as pd
-from plotly import graph_objs as go
-from plotly import io as pio
-from plotly import offline
+from plotly import graph_objs as go
+from plotly import io as pio
+from plotly import offline
 
-from jmetal.lab.visualization.plotting import Plot
+from jmetal.lab.visualization.plotting import Plot
 
 LOGGER = logging.getLogger('jmetal')
 
@@ -129,7 +126,7 @@ 
    Source code for jmetal.lab.visualization.interactive
    [docs]class InteractivePlot(Plot):
 
-    def __init__(self,
+    def __init__(self,
                  title: str = 'Pareto front approximation',
                  reference_front: List[S] = None,
                  reference_point: list = None,
diff --git a/docs/_modules/jmetal/lab/visualization/plotting.html b/docs/_modules/jmetal/lab/visualization/plotting.html
index 3870466b..bc1001de 100644
--- a/docs/_modules/jmetal/lab/visualization/plotting.html
+++ b/docs/_modules/jmetal/lab/visualization/plotting.html
@@ -24,7 +24,6 @@
     
     
     
-    
     
     
   
@@ -74,8 +73,6 @@ 
    Table Of Contents
    
 
  • Single-objective algorithms
    • 
 
  • Operators
    • 
 
  • Problems
    • 
-
  • Contributing
    • 
-
  • About
    • 
 
 
     
@@ -113,12 +110,12 @@ 
    Table Of Contents
    
             
   
    Source code for jmetal.lab.visualization.plotting
     import logging
-from typing import TypeVar, List, Tuple
+from typing import TypeVar, List, Tuple
 
 import numpy as np
 import pandas as pd
-from matplotlib import pyplot as plt
-from pandas import plotting
+from matplotlib import pyplot as plt
+from pandas import plotting
 
 LOGGER = logging.getLogger('jmetal')
 
@@ -127,7 +124,7 @@ 
    Source code for jmetal.lab.visualization.plotting
    [docs]class Plot:
 
-    def __init__(self,
+    def __init__(self,
                  title: str = 'Pareto front approximation',
                  reference_front: List[S] = None,
                  reference_point: list = None,
diff --git a/docs/_modules/jmetal/lab/visualization/posterior.html b/docs/_modules/jmetal/lab/visualization/posterior.html
index d505f207..53ebc92b 100644
--- a/docs/_modules/jmetal/lab/visualization/posterior.html
+++ b/docs/_modules/jmetal/lab/visualization/posterior.html
@@ -24,7 +24,6 @@
     
     
     
-    
     
     
   
@@ -74,8 +73,6 @@ 
    Table Of Contents
    
 
  • Single-objective algorithms
    • 
 
  • Operators
    • 
 
  • Problems
    • 
-
  • Contributing
    • 
-
  • About
    • 
 
 
     
@@ -114,7 +111,7 @@ 
    Table Of Contents
    
   
    Source code for jmetal.lab.visualization.posterior
     import numpy as np
 import pandas as pd
-from matplotlib import pyplot as plt
+from matplotlib import pyplot as plt
 
 
 
    [docs]def plot_posterior(sample, higher_is_better: bool = False, min_points_per_hexbin: int = 2, alg_names: list = None,
diff --git a/docs/_modules/jmetal/lab/visualization/streaming.html b/docs/_modules/jmetal/lab/visualization/streaming.html
index 5eb1796c..c15bbd91 100644
--- a/docs/_modules/jmetal/lab/visualization/streaming.html
+++ b/docs/_modules/jmetal/lab/visualization/streaming.html
@@ -24,7 +24,6 @@
     
     
     
-    
     
     
   
@@ -74,8 +73,6 @@ 
    Table Of Contents
    
 
  • Single-objective algorithms
    • 
 
  • Operators
    • 
 
  • Problems
    • 
-
  • Contributing
    • 
-
  • About
    • 
 
 
     
@@ -113,13 +110,13 @@ 
    Table Of Contents
    
             
   
    Source code for jmetal.lab.visualization.streaming
     import logging
-from typing import TypeVar, List
+from typing import TypeVar, List
 
 import matplotlib
-from matplotlib import pyplot as plt
-from mpl_toolkits.mplot3d import Axes3D
+from matplotlib import pyplot as plt
+from mpl_toolkits.mplot3d import Axes3D
 
-from jmetal.lab.visualization.plotting import Plot
+from jmetal.lab.visualization.plotting import Plot
 
 LOGGER = logging.getLogger('jmetal')
 
@@ -136,7 +133,7 @@ 
    Source code for jmetal.lab.visualization.streaming
    [docs]class StreamingPlot:
 
-    def __init__(self,
+    def __init__(self,
                  plot_title: str = 'Pareto front approximation',
                  reference_front: List[S] = None,
                  reference_point: list = None,
diff --git a/docs/_modules/jmetal/operator/crossover.html b/docs/_modules/jmetal/operator/crossover.html
index 228c0492..e41f15ee 100644
--- a/docs/_modules/jmetal/operator/crossover.html
+++ b/docs/_modules/jmetal/operator/crossover.html
@@ -24,7 +24,6 @@
     
     
     
-    
     
     
   
@@ -74,8 +73,6 @@ 
    Table Of Contents
    
 
  • Single-objective algorithms
    • 
 
  • Operators
    • 
 
  • Problems
    • 
-
  • Contributing
    • 
-
  • About
    • 
 
 
     
@@ -114,10 +111,10 @@ 
    Table Of Contents
    
   
    Source code for jmetal.operator.crossover
     import copy
 import random
-from typing import List
+from typing import List
 
-from jmetal.core.operator import Crossover
-from jmetal.core.solution import Solution, FloatSolution, BinarySolution, PermutationSolution
+from jmetal.core.operator import Crossover
+from jmetal.core.solution import Solution, FloatSolution, BinarySolution, PermutationSolution
 
 """
 .. module:: crossover
@@ -130,7 +127,7 @@ 
    Source code for jmetal.operator.crossover
     
 
    [docs]class NullCrossover(Crossover[Solution, Solution]):
 
-    def __init__(self):
+    def __init__(self):
         super(NullCrossover, self).__init__(probability=0.0)
 
 
    [docs]    def execute(self, parents: List[Solution]) -> List[Solution]:
@@ -151,7 +148,7 @@ 
    Source code for jmetal.operator.crossover
     
 
    [docs]class PMXCrossover(Crossover[PermutationSolution, PermutationSolution]):
 
-    def __init__(self, probability: float):
+    def __init__(self, probability: float):
         super(PMXCrossover, self).__init__(probability=probability)
 
 
    [docs]    def execute(self, parents: List[PermutationSolution]) -> List[PermutationSolution]:
@@ -210,7 +207,7 @@ 
    Source code for jmetal.operator.crossover
     
 
    [docs]class CXCrossover(Crossover[PermutationSolution, PermutationSolution]):
 
-    def __init__(self, probability: float):
+    def __init__(self, probability: float):
         super(CXCrossover, self).__init__(probability=probability)
 
 
    [docs]    def execute(self, parents: List[PermutationSolution]) -> List[PermutationSolution]:
@@ -253,7 +250,7 @@ 
    Source code for jmetal.operator.crossover
     
    [docs]class SBXCrossover(Crossover[FloatSolution, FloatSolution]):
     __EPS = 1.0e-14
 
-    def __init__(self, probability: float, distribution_index: float = 20.0):
+    def __init__(self, probability: float, distribution_index: float = 20.0):
         super(SBXCrossover, self).__init__(probability=probability)
         self.distribution_index = distribution_index
 
@@ -332,7 +329,7 @@ 
    Source code for jmetal.operator.crossover
     
 
    [docs]class SPXCrossover(Crossover[BinarySolution, BinarySolution]):
 
-    def __init__(self, probability: float):
+    def __init__(self, probability: float):
         super(SPXCrossover, self).__init__(probability=probability)
 
 
    [docs]    def execute(self, parents: List[BinarySolution]) -> List[BinarySolution]:
@@ -396,7 +393,7 @@ 
    Source code for jmetal.operator.crossover
         external to the crossover operator.
     """
 
-    def __init__(self, CR: float, F: float, K: float):
+    def __init__(self, CR: float, F: float, K: float):
         super(DifferentialEvolutionCrossover, self).__init__(probability=1.0)
         self.CR = CR
         self.F = F
diff --git a/docs/_modules/jmetal/operator/mutation.html b/docs/_modules/jmetal/operator/mutation.html
index 82b0da44..5d761a86 100644
--- a/docs/_modules/jmetal/operator/mutation.html
+++ b/docs/_modules/jmetal/operator/mutation.html
@@ -24,7 +24,6 @@
     
     
     
-    
     
     
   
@@ -74,8 +73,6 @@ 
    Table Of Contents
    
 
  • Single-objective algorithms
    • 
 
  • Operators
    • 
 
  • Problems
    • 
-
  • Contributing
    • 
-
  • About
    • 
 
 
     
@@ -114,8 +111,8 @@ 
    Table Of Contents
    
   
    Source code for jmetal.operator.mutation
     import random
 
-from jmetal.core.operator import Mutation
-from jmetal.core.solution import BinarySolution, Solution, FloatSolution, IntegerSolution, PermutationSolution
+from jmetal.core.operator import Mutation
+from jmetal.core.solution import BinarySolution, Solution, FloatSolution, IntegerSolution, PermutationSolution
 
 """
 .. module:: mutation
@@ -128,7 +125,7 @@ 
    Source code for jmetal.operator.mutation
     
 
    [docs]class NullMutation(Mutation[Solution]):
 
-    def __init__(self):
+    def __init__(self):
         super(NullMutation, self).__init__(probability=0)
 
 
    [docs]    def execute(self, solution: Solution) -> Solution:
@@ -140,7 +137,7 @@ 
    Source code for jmetal.operator.mutation
     
 
    [docs]class BitFlipMutation(Mutation[BinarySolution]):
 
-    def __init__(self, probability: float):
+    def __init__(self, probability: float):
         super(BitFlipMutation, self).__init__(probability=probability)
 
 
    [docs]    def execute(self, solution: BinarySolution) -> BinarySolution:
@@ -158,7 +155,7 @@ 
    Source code for jmetal.operator.mutation
     
 
    [docs]class PolynomialMutation(Mutation[FloatSolution]):
 
-    def __init__(self, probability: float, distribution_index: float = 0.20):
+    def __init__(self, probability: float, distribution_index: float = 0.20):
         super(PolynomialMutation, self).__init__(probability=probability)
         self.distribution_index = distribution_index
 
@@ -202,7 +199,7 @@ 
    Source code for jmetal.operator.mutation
     
 
    [docs]class IntegerPolynomialMutation(Mutation[IntegerSolution]):
 
-    def __init__(self, probability: float, distribution_index: float = 0.20):
+    def __init__(self, probability: float, distribution_index: float = 0.20):
         super(IntegerPolynomialMutation, self).__init__(probability=probability)
         self.distribution_index = distribution_index
 
@@ -243,7 +240,7 @@ 
    Source code for jmetal.operator.mutation
     
 
    [docs]class SimpleRandomMutation(Mutation[FloatSolution]):
 
-    def __init__(self, probability: float):
+    def __init__(self, probability: float):
         super(SimpleRandomMutation, self).__init__(probability=probability)
 
 
    [docs]    def execute(self, solution: FloatSolution) -> FloatSolution:
@@ -260,7 +257,7 @@ 
    Source code for jmetal.operator.mutation
     
 
    [docs]class UniformMutation(Mutation[FloatSolution]):
 
-    def __init__(self, probability: float, perturbation: float = 0.5):
+    def __init__(self, probability: float, perturbation: float = 0.5):
         super(UniformMutation, self).__init__(probability=probability)
         self.perturbation = perturbation
 
@@ -287,7 +284,7 @@ 
    Source code for jmetal.operator.mutation
     
 
    [docs]class NonUniformMutation(Mutation[FloatSolution]):
 
-    def __init__(self, probability: float, perturbation: float = 0.5, max_iterations: int = 0.5):
+    def __init__(self, probability: float, perturbation: float = 0.5, max_iterations: int = 0.5):
         super(NonUniformMutation, self).__init__(probability=probability)
         self.perturbation = perturbation
         self.max_iterations = max_iterations
diff --git a/docs/_modules/jmetal/operator/selection.html b/docs/_modules/jmetal/operator/selection.html
index de61c290..842f1c43 100644
--- a/docs/_modules/jmetal/operator/selection.html
+++ b/docs/_modules/jmetal/operator/selection.html
@@ -24,7 +24,6 @@
     
     
     
-    
     
     
   
@@ -74,8 +73,6 @@ 
    Table Of Contents
    
 
  • Single-objective algorithms
    • 
 
  • Operators
    • 
 
  • Problems
    • 
-
  • Contributing
    • 
-
  • About
    • 
 
 
     
@@ -113,14 +110,14 @@ 
    Table Of Contents
    
             
   
    Source code for jmetal.operator.selection
     import random
-from typing import List, TypeVar
+from typing import List, TypeVar
 
 import numpy as np
 
-from jmetal.core.operator import Selection
-from jmetal.util.density_estimator import CrowdingDistance
-from jmetal.util.ranking import FastNonDominatedRanking
-from jmetal.util.comparator import Comparator, DominanceComparator
+from jmetal.core.operator import Selection
+from jmetal.util.density_estimator import CrowdingDistance
+from jmetal.util.ranking import FastNonDominatedRanking
+from jmetal.util.comparator import Comparator, DominanceComparator
 
 S = TypeVar('S')
 
@@ -137,7 +134,7 @@ 
    Source code for jmetal.operator.selection
         """Performs roulette wheel selection.
     """
 
-    def __init__(self):
+    def __init__(self):
         super(RouletteWheelSelection).__init__()
 
 
    [docs]    def execute(self, front: List[S]) -> S:
@@ -164,7 +161,7 @@ 
    Source code for jmetal.operator.selection
     
 
    [docs]class BinaryTournamentSelection(Selection[List[S], S]):
 
-    def __init__(self, comparator: Comparator = DominanceComparator()):
+    def __init__(self, comparator: Comparator = DominanceComparator()):
         super(BinaryTournamentSelection, self).__init__()
         self.comparator = comparator
 
@@ -199,7 +196,7 @@ 
    Source code for jmetal.operator.selection
     
 
    [docs]class BestSolutionSelection(Selection[List[S], S]):
 
-    def __init__(self):
+    def __init__(self):
         super(BestSolutionSelection, self).__init__()
 
 
    [docs]    def execute(self, front: List[S]) -> S:
@@ -222,7 +219,7 @@ 
    Source code for jmetal.operator.selection
     
 
    [docs]class NaryRandomSolutionSelection(Selection[List[S], S]):
 
-    def __init__(self, number_of_solutions_to_be_returned: int = 1):
+    def __init__(self, number_of_solutions_to_be_returned: int = 1):
         super(NaryRandomSolutionSelection, self).__init__()
         if number_of_solutions_to_be_returned < 0:
             raise Exception('The number of solutions to be returned must be positive integer')
@@ -246,7 +243,7 @@ 
    Source code for jmetal.operator.selection
     
 
    [docs]class DifferentialEvolutionSelection(Selection[List[S], List[S]]):
 
-    def __init__(self):
+    def __init__(self):
         super(DifferentialEvolutionSelection, self).__init__()
         self.index_to_exclude = None
 
@@ -273,7 +270,7 @@ 
    Source code for jmetal.operator.selection
     
 
    [docs]class RandomSolutionSelection(Selection[List[S], S]):
 
-    def __init__(self):
+    def __init__(self):
         super(RandomSolutionSelection, self).__init__()
 
 
    [docs]    def execute(self, front: List[S]) -> S:
@@ -290,7 +287,7 @@ 
    Source code for jmetal.operator.selection
     
 
    [docs]class RankingAndCrowdingDistanceSelection(Selection[List[S], List[S]]):
 
-    def __init__(self, max_population_size: int, dominance_comparator: Comparator = DominanceComparator()):
+    def __init__(self, max_population_size: int, dominance_comparator: Comparator = DominanceComparator()):
         super(RankingAndCrowdingDistanceSelection, self).__init__()
         self.max_population_size = max_population_size
         self.dominance_comparator = dominance_comparator
@@ -327,7 +324,7 @@ 
    Source code for jmetal.operator.selection
     
 
    [docs]class RankingAndFitnessSelection(Selection[List[S], List[S]]):
 
-    def __init__(self,
+    def __init__(self,
                  max_population_size: int, reference_point: S,
                  dominance_comparator: Comparator = DominanceComparator()):
         super(RankingAndFitnessSelection, self).__init__()
@@ -417,7 +414,7 @@ 
    Source code for jmetal.operator.selection
     
 
    [docs]class BinaryTournament2Selection(Selection[List[S], S]):
 
-    def __init__(self, comparator_list: List[Comparator]):
+    def __init__(self, comparator_list: List[Comparator]):
         super(BinaryTournament2Selection, self).__init__()
         self.comparator_list = comparator_list
 
diff --git a/docs/_modules/jmetal/problem/multiobjective/constrained.html b/docs/_modules/jmetal/problem/multiobjective/constrained.html
index ee622382..3a2329bd 100644
--- a/docs/_modules/jmetal/problem/multiobjective/constrained.html
+++ b/docs/_modules/jmetal/problem/multiobjective/constrained.html
@@ -24,7 +24,6 @@
     
     
     
-    
     
     
   
@@ -74,8 +73,6 @@ 
    Table Of Contents
    
 
  • Single-objective algorithms
    • 
 
  • Operators
    • 
 
  • Problems
    • 
-
  • Contributing
    • 
-
  • About
    • 
 
 
     
@@ -112,10 +109,10 @@ 
    Table Of Contents
    
           
    
             
   
    Source code for jmetal.problem.multiobjective.constrained
    -from math import pi, cos, atan
+from math import pi, cos, atan
 
-from jmetal.core.problem import FloatProblem
-from jmetal.core.solution import FloatSolution
+from jmetal.core.problem import FloatProblem
+from jmetal.core.solution import FloatSolution
 
 """
 .. module:: constrained
@@ -129,7 +126,7 @@ 
    Source code for jmetal.problem.multiobjective.constrained
    [docs]class Srinivas(FloatProblem):
     """ Class representing problem Srinivas. """
 
-    def __init__(self):
+    def __init__(self):
         super(Srinivas, self).__init__()
         self.number_of_variables = 2
         self.number_of_objectives = 2
@@ -166,7 +163,7 @@ 
    Source code for jmetal.problem.multiobjective.constrained
    [docs]class Tanaka(FloatProblem):
     """ Class representing problem Tanaka. """
 
-    def __init__(self):
+    def __init__(self):
         super(Tanaka, self).__init__()
         self.number_of_variables = 2
         self.number_of_objectives = 2
@@ -208,7 +205,7 @@ 
    Source code for jmetal.problem.multiobjective.constrained
    [docs]class Osyczka2(FloatProblem):
     """ Class representing problem Osyczka2. """
 
-    def __init__(self):
+    def __init__(self):
         super(Osyczka2, self).__init__()
         self.number_of_variables = 6
         self.number_of_objectives = 2
@@ -258,7 +255,7 @@ 
    Source code for jmetal.problem.multiobjective.constrained
    [docs]class Binh2(FloatProblem):
     """ Class representing problem Binh2. """
 
-    def __init__(self):
+    def __init__(self):
         super(Binh2, self).__init__()
         self.number_of_variables = 2
         self.number_of_objectives = 2
diff --git a/docs/_modules/jmetal/problem/multiobjective/dtlz.html b/docs/_modules/jmetal/problem/multiobjective/dtlz.html
index dc10b458..642dd83a 100644
--- a/docs/_modules/jmetal/problem/multiobjective/dtlz.html
+++ b/docs/_modules/jmetal/problem/multiobjective/dtlz.html
@@ -24,7 +24,6 @@
     
     
     
-    
     
     
   
@@ -74,8 +73,6 @@ 
    Table Of Contents
    
 
  • Single-objective algorithms
    • 
 
  • Operators
    • 
 
  • Problems
    • 
-
  • Contributing
    • 
-
  • About
    • 
 
 
     
@@ -112,10 +109,10 @@ 
    Table Of Contents
    
           
    
             
   
    Source code for jmetal.problem.multiobjective.dtlz
    -from math import pi, cos, sin
+from math import pi, cos, sin
 
-from jmetal.core.problem import FloatProblem
-from jmetal.core.solution import FloatSolution
+from jmetal.core.problem import FloatProblem
+from jmetal.core.solution import FloatSolution
 
 """
 .. module:: DTLZ
@@ -132,7 +129,7 @@ 
    Source code for jmetal.problem.multiobjective.dtlz
        .. note:: Unconstrained problem. The default number of variables and objectives are, respectively, 7 and 3.
     """
 
-    def __init__(self, number_of_variables: int = 7, number_of_objectives=3):
+    def __init__(self, number_of_variables: int = 7, number_of_objectives=3):
         """ :param number_of_variables: number of decision variables of the problem.
         """
         super(DTLZ1, self).__init__()
@@ -175,7 +172,7 @@ 
    Source code for jmetal.problem.multiobjective.dtlz
        .. note:: Unconstrained problem. The default number of variables and objectives are, respectively, 12 and 3.
     """
 
-    def __init__(self, number_of_variables: int = 12, number_of_objectives=3):
+    def __init__(self, number_of_variables: int = 12, number_of_objectives=3):
         """:param number_of_variables: number of decision variables of the problem
         """
         super(DTLZ2, self).__init__(number_of_variables, number_of_objectives)
@@ -206,7 +203,7 @@ 
    Source code for jmetal.problem.multiobjective.dtlz
        .. note:: Unconstrained problem. The default number of variables and objectives are, respectively, 12 and 3.
     """
 
-    def __init__(self, number_of_variables: int = 12, number_of_objectives=3):
+    def __init__(self, number_of_variables: int = 12, number_of_objectives=3):
         """:param number_of_variables: number of decision variables of the problem
         """
         super(DTLZ3, self).__init__(number_of_variables, number_of_objectives)
@@ -241,7 +238,7 @@ 
    Source code for jmetal.problem.multiobjective.dtlz
        .. note:: Unconstrained problem. The default number of variables and objectives are, respectively, 12 and 3.
     """
 
-    def __init__(self, number_of_variables: int = 12, number_of_objectives=3):
+    def __init__(self, number_of_variables: int = 12, number_of_objectives=3):
         """:param number_of_variables: number of decision variables of the problem
         """
         super(DTLZ4, self).__init__(number_of_variables, number_of_objectives)
@@ -275,7 +272,7 @@ 
    Source code for jmetal.problem.multiobjective.dtlz
        .. note:: Unconstrained problem. The default number of variables and objectives are, respectively, 12 and 3.
     """
 
-    def __init__(self, number_of_variables: int = 12, number_of_objectives=3):
+    def __init__(self, number_of_variables: int = 12, number_of_objectives=3):
         """:param number_of_variables: number of decision variables of the problem
         """
         super(DTLZ5, self).__init__(number_of_variables, number_of_objectives)
@@ -314,7 +311,7 @@ 
    Source code for jmetal.problem.multiobjective.dtlz
        .. note:: Unconstrained problem. The default number of variables and objectives are, respectively, 12 and 3.
     """
 
-    def __init__(self, number_of_variables: int = 12, number_of_objectives=3):
+    def __init__(self, number_of_variables: int = 12, number_of_objectives=3):
         """:param number_of_variables: number of decision variables of the problem
         """
         super(DTLZ6, self).__init__(number_of_variables, number_of_objectives)
@@ -353,7 +350,7 @@ 
    Source code for jmetal.problem.multiobjective.dtlz
        .. note:: Unconstrained problem. The default number of variables and objectives are, respectively, 22 and 3.
     """
 
-    def __init__(self, number_of_variables: int = 22, number_of_objectives=3):
+    def __init__(self, number_of_variables: int = 22, number_of_objectives=3):
         """:param number_of_variables: number of decision variables of the problem
         """
         super(DTLZ7, self).__init__(number_of_variables, number_of_objectives)
diff --git a/docs/_modules/jmetal/problem/multiobjective/fda.html b/docs/_modules/jmetal/problem/multiobjective/fda.html
index 00724d3a..77328f64 100644
--- a/docs/_modules/jmetal/problem/multiobjective/fda.html
+++ b/docs/_modules/jmetal/problem/multiobjective/fda.html
@@ -24,7 +24,6 @@
     
     
     
-    
     
     
   
@@ -74,8 +73,6 @@ 
    Table Of Contents
    
 
  • Single-objective algorithms
    • 
 
  • Operators
    • 
 
  • Problems
    • 
-
  • Contributing
    • 
-
  • About
    • 
 
 
     
@@ -112,13 +109,13 @@ 
    Table Of Contents
    
           
    
             
   
    Source code for jmetal.problem.multiobjective.fda
    -from abc import ABC, abstractmethod
-from math import sqrt, pow, sin, pi, floor, cos
+from abc import ABC, abstractmethod
+from math import sqrt, pow, sin, pi, floor, cos
 
 import numpy
 
-from jmetal.core.problem import FloatProblem, DynamicProblem
-from jmetal.core.solution import FloatSolution
+from jmetal.core.problem import FloatProblem, DynamicProblem
+from jmetal.core.solution import FloatSolution
 
 """
 .. module:: FDA
@@ -130,7 +127,7 @@ 
    Source code for jmetal.problem.multiobjective.fda
    [docs]class FDA(DynamicProblem, FloatProblem, ABC):
-    def __init__(self):
+    def __init__(self):
         super(FDA, self).__init__()
         self.tau_T = 5
         self.nT = 10
@@ -159,7 +156,7 @@ 
    Source code for jmetal.problem.multiobjective.fda
        .. note:: Bi-objective dynamic unconstrained problem. The default number of variables is 100.
     """
 
-    def __init__(self, number_of_variables: int = 100):
+    def __init__(self, number_of_variables: int = 100):
         """ :param number_of_variables: Number of decision variables of the problem.
         """
         super(FDA1, self).__init__()
@@ -203,7 +200,7 @@ 
    Source code for jmetal.problem.multiobjective.fda
        .. note:: Bi-objective dynamic unconstrained problem. The default number of variables is 31.
     """
 
-    def __init__(self, number_of_variables: int = 31):
+    def __init__(self, number_of_variables: int = 31):
         """ :param number_of_variables: Number of decision variables of the problem.
         """
         super(FDA2, self).__init__()
@@ -248,7 +245,7 @@ 
    Source code for jmetal.problem.multiobjective.fda
        .. note:: Bi-objective dynamic unconstrained problem. The default number of variables is 30.
     """
 
-    def __init__(self, number_of_variables: int = 30):
+    def __init__(self, number_of_variables: int = 30):
         """ :param number_of_variables: Number of decision variables of the problem.
         """
         super(FDA3, self).__init__()
@@ -306,7 +303,7 @@ 
    Source code for jmetal.problem.multiobjective.fda
        """
     M = 3
 
-    def __init__(self, number_of_variables: int = 12):
+    def __init__(self, number_of_variables: int = 12):
         """ :param number_of_variables: Number of decision variables of the problem.
         """
         super(FDA4, self).__init__()
@@ -365,7 +362,7 @@ 
    Source code for jmetal.problem.multiobjective.fda
        """
     M = 3
 
-    def __init__(self, number_of_variables: int = 12):
+    def __init__(self, number_of_variables: int = 12):
         """ :param number_of_variables: Number of decision variables of the problem.
         """
         super(FDA5, self).__init__()
diff --git a/docs/_modules/jmetal/problem/multiobjective/lircmop.html b/docs/_modules/jmetal/problem/multiobjective/lircmop.html
index d8b792e6..073f3d67 100644
--- a/docs/_modules/jmetal/problem/multiobjective/lircmop.html
+++ b/docs/_modules/jmetal/problem/multiobjective/lircmop.html
@@ -24,7 +24,6 @@
     
     
     
-    
     
     
   
@@ -74,8 +73,6 @@ 
    Table Of Contents
    
 
  • Single-objective algorithms
    • 
 
  • Operators
    • 
 
  • Problems
    • 
-
  • Contributing
    • 
-
  • About
    • 
 
 
     
@@ -112,10 +109,10 @@ 
    Table Of Contents
    
           
    
             
   
    Source code for jmetal.problem.multiobjective.lircmop
    -from math import sin, pi, cos, sqrt
+from math import sin, pi, cos, sqrt
 
-from jmetal.core.problem import FloatProblem
-from jmetal.core.solution import FloatSolution
+from jmetal.core.problem import FloatProblem
+from jmetal.core.solution import FloatSolution
 
 
 
    [docs]class LIRCMOP1(FloatProblem):
@@ -125,7 +122,7 @@ 
    Source code for jmetal.problem.multiobjective.lircmop
          Fan, Z., Li, W., Cai, X. et al. Soft Comput (2019). https://doi.org/10.1007/s00500-019-03794-x
     """
 
-    def __init__(self, number_of_variables: int = 30):
+    def __init__(self, number_of_variables: int = 30):
         super(LIRCMOP1, self).__init__()
         self.number_of_variables = number_of_variables
         self.number_of_objectives = 2
@@ -186,7 +183,7 @@ 
    Source code for jmetal.problem.multiobjective.lircmop
          Fan, Z., Li, W., Cai, X. et al. Soft Comput (2019). https://doi.org/10.1007/s00500-019-03794-x
     """
 
-    def __init__(self, number_of_variables: int = 30):
+    def __init__(self, number_of_variables: int = 30):
         super(LIRCMOP2, self).__init__(number_of_variables)
 
 
    [docs]    def evaluate(self, solution: FloatSolution) -> FloatSolution:
@@ -210,7 +207,7 @@ 
    Source code for jmetal.problem.multiobjective.lircmop
          Fan, Z., Li, W., Cai, X. et al. Soft Comput (2019). https://doi.org/10.1007/s00500-019-03794-x
     """
 
-    def __init__(self, number_of_variables: int = 30):
+    def __init__(self, number_of_variables: int = 30):
         super(LIRCMOP3, self).__init__(number_of_variables)
 
 
    [docs]    def evaluate_constraints(self, solution: FloatSolution) -> FloatSolution:
@@ -240,7 +237,7 @@ 
    Source code for jmetal.problem.multiobjective.lircmop
          Fan, Z., Li, W., Cai, X. et al. Soft Comput (2019). https://doi.org/10.1007/s00500-019-03794-x
     """
 
-    def __init__(self, number_of_variables: int = 30):
+    def __init__(self, number_of_variables: int = 30):
         super(LIRCMOP4, self).__init__(number_of_variables)
 
 
    [docs]    def evaluate_constraints(self, solution: FloatSolution) -> FloatSolution:
@@ -270,7 +267,7 @@ 
    Source code for jmetal.problem.multiobjective.lircmop
          Fan, Z., Li, W., Cai, X. et al. Soft Comput (2019). https://doi.org/10.1007/s00500-019-03794-x
     """
 
-    def __init__(self, number_of_variables: int = 30):
+    def __init__(self, number_of_variables: int = 30):
         super(LIRCMOP5, self).__init__()
         self.number_of_variables = number_of_variables
         self.number_of_objectives = 2
@@ -340,7 +337,7 @@ 
    Source code for jmetal.problem.multiobjective.lircmop
          Fan, Z., Li, W., Cai, X. et al. Soft Comput (2019). https://doi.org/10.1007/s00500-019-03794-x
     """
 
-    def __init__(self, number_of_variables: int = 30):
+    def __init__(self, number_of_variables: int = 30):
         super(LIRCMOP6, self).__init__(number_of_variables)
 
 
    [docs]    def evaluate(self, solution: FloatSolution) -> FloatSolution:
@@ -387,7 +384,7 @@ 
    Source code for jmetal.problem.multiobjective.lircmop
          Fan, Z., Li, W., Cai, X. et al. Soft Comput (2019). https://doi.org/10.1007/s00500-019-03794-x
     """
 
-    def __init__(self, number_of_variables: int = 30):
+    def __init__(self, number_of_variables: int = 30):
         super(LIRCMOP7, self).__init__(number_of_variables)
 
 
    [docs]    def evaluate_constraints(self, solution: FloatSolution) -> FloatSolution:
@@ -424,7 +421,7 @@ 
    Source code for jmetal.problem.multiobjective.lircmop
          Fan, Z., Li, W., Cai, X. et al. Soft Comput (2019). https://doi.org/10.1007/s00500-019-03794-x
     """
 
-    def __init__(self, number_of_variables: int = 30):
+    def __init__(self, number_of_variables: int = 30):
         super(LIRCMOP8, self).__init__(number_of_variables)
 
 
    [docs]    def evaluate_constraints(self, solution: FloatSolution) -> FloatSolution:
@@ -461,7 +458,7 @@ 
    Source code for jmetal.problem.multiobjective.lircmop
          Fan, Z., Li, W., Cai, X. et al. Soft Comput (2019). https://doi.org/10.1007/s00500-019-03794-x
     """
 
-    def __init__(self, number_of_variables: int = 30):
+    def __init__(self, number_of_variables: int = 30):
         super(LIRCMOP9, self).__init__(number_of_variables)
 
 
    [docs]    def evaluate(self, solution: FloatSolution) -> FloatSolution:
@@ -509,7 +506,7 @@ 
    Source code for jmetal.problem.multiobjective.lircmop
          Fan, Z., Li, W., Cai, X. et al. Soft Comput (2019). https://doi.org/10.1007/s00500-019-03794-x
     """
 
-    def __init__(self, number_of_variables: int = 30):
+    def __init__(self, number_of_variables: int = 30):
         super(LIRCMOP10, self).__init__(number_of_variables)
 
 
    [docs]    def evaluate(self, solution: FloatSolution) -> FloatSolution:
@@ -556,7 +553,7 @@ 
    Source code for jmetal.problem.multiobjective.lircmop
          Fan, Z., Li, W., Cai, X. et al. Soft Comput (2019). https://doi.org/10.1007/s00500-019-03794-x
     """
 
-    def __init__(self, number_of_variables: int = 30):
+    def __init__(self, number_of_variables: int = 30):
         super(LIRCMOP11, self).__init__(number_of_variables)
 
 
    [docs]    def evaluate_constraints(self, solution: FloatSolution) -> FloatSolution:
@@ -593,7 +590,7 @@ 
    Source code for jmetal.problem.multiobjective.lircmop
          Fan, Z., Li, W., Cai, X. et al. Soft Comput (2019). https://doi.org/10.1007/s00500-019-03794-x
     """
 
-    def __init__(self, number_of_variables: int = 30):
+    def __init__(self, number_of_variables: int = 30):
         super(LIRCMOP12, self).__init__(number_of_variables)
 
 
    [docs]    def evaluate_constraints(self, solution: FloatSolution) -> FloatSolution:
@@ -630,7 +627,7 @@ 
    Source code for jmetal.problem.multiobjective.lircmop
          Fan, Z., Li, W., Cai, X. et al. Soft Comput (2019). https://doi.org/10.1007/s00500-019-03794-x
     """
 
-    def __init__(self, number_of_variables: int = 30):
+    def __init__(self, number_of_variables: int = 30):
         super(LIRCMOP13, self).__init__()
         self.number_of_variables = number_of_variables
         self.number_of_objectives = 3
@@ -683,7 +680,7 @@ 
    Source code for jmetal.problem.multiobjective.lircmop
          Fan, Z., Li, W., Cai, X. et al. Soft Comput (2019). https://doi.org/10.1007/s00500-019-03794-x
     """
 
-    def __init__(self, number_of_variables: int = 30):
+    def __init__(self, number_of_variables: int = 30):
         super(LIRCMOP14, self).__init__(number_of_variables)
         self.number_of_constraints = 3
 
diff --git a/docs/_modules/jmetal/problem/multiobjective/lz09.html b/docs/_modules/jmetal/problem/multiobjective/lz09.html
index f5d7104d..e2e54ab9 100644
--- a/docs/_modules/jmetal/problem/multiobjective/lz09.html
+++ b/docs/_modules/jmetal/problem/multiobjective/lz09.html
@@ -24,7 +24,6 @@
     
     
     
-    
     
     
   
@@ -74,8 +73,6 @@ 
    Table Of Contents
    
 
  • Single-objective algorithms
    • 
 
  • Operators
    • 
 
  • Problems
    • 
-
  • Contributing
    • 
-
  • About
    • 
 
 
     
@@ -113,10 +110,10 @@ 
    Table Of Contents
    
             
   
    Source code for jmetal.problem.multiobjective.lz09
     import math
-from abc import ABCMeta
+from abc import ABCMeta
 
-from jmetal.core.problem import FloatProblem
-from jmetal.core.solution import FloatSolution
+from jmetal.core.problem import FloatProblem
+from jmetal.core.solution import FloatSolution
 
 """
 .. module:: LZ09
@@ -131,7 +128,7 @@ 
    Source code for jmetal.problem.multiobjective.lz09
    __metaclass__ = ABCMeta
 
-    def __init__(self,
+    def __init__(self,
                  number_of_variables: int,
                  number_of_objectives: int,
                  number_of_constraints: int,
@@ -387,7 +384,7 @@ 
    Source code for jmetal.problem.multiobjective.lz09
    [docs]class LZ09_F1(LZ09):
 
-    def __init__(self):
+    def __init__(self):
         super(LZ09_F1, self).__init__(number_of_variables=10, number_of_objectives=2, number_of_constraints=0,
                                       dtype=1, ltype=21, ptype=21)
 
@@ -397,7 +394,7 @@ 
    Source code for jmetal.problem.multiobjective.lz09
    [docs]class LZ09_F2(LZ09):
 
-    def __init__(self):
+    def __init__(self):
         super(LZ09_F2, self).__init__(number_of_variables=30, number_of_objectives=2, number_of_constraints=0,
                                       dtype=1, ltype=22, ptype=21)
 
@@ -407,7 +404,7 @@ 
    Source code for jmetal.problem.multiobjective.lz09
    [docs]class LZ09_F3(LZ09):
 
-    def __init__(self):
+    def __init__(self):
         super(LZ09_F3, self).__init__(number_of_variables=30, number_of_objectives=2, number_of_constraints=0,
                                       dtype=1, ltype=23, ptype=21)
 
@@ -417,7 +414,7 @@ 
    Source code for jmetal.problem.multiobjective.lz09
    [docs]class LZ09_F4(LZ09):
 
-    def __init__(self):
+    def __init__(self):
         super(LZ09_F4, self).__init__(number_of_variables=30, number_of_objectives=2, number_of_constraints=0,
                                       dtype=1, ltype=24, ptype=21)
 
@@ -427,7 +424,7 @@ 
    Source code for jmetal.problem.multiobjective.lz09
    [docs]class LZ09_F5(LZ09):
 
-    def __init__(self):
+    def __init__(self):
         super(LZ09_F5, self).__init__(number_of_variables=30, number_of_objectives=2, number_of_constraints=0,
                                       dtype=1, ltype=26, ptype=21)
 
@@ -437,7 +434,7 @@ 
    Source code for jmetal.problem.multiobjective.lz09
    [docs]class LZ09_F6(LZ09):
 
-    def __init__(self):
+    def __init__(self):
         super(LZ09_F6, self).__init__(number_of_variables=10, number_of_objectives=3, number_of_constraints=0,
                                       dtype=1, ltype=32, ptype=31)
 
@@ -447,7 +444,7 @@ 
    Source code for jmetal.problem.multiobjective.lz09
    [docs]class LZ09_F7(LZ09):
 
-    def __init__(self):
+    def __init__(self):
         super(LZ09_F7, self).__init__(number_of_variables=10, number_of_objectives=2, number_of_constraints=0,
                                       dtype=3, ltype=21, ptype=21)
 
@@ -457,7 +454,7 @@ 
    Source code for jmetal.problem.multiobjective.lz09
    [docs]class LZ09_F8(LZ09):
 
-    def __init__(self):
+    def __init__(self):
         super(LZ09_F8, self).__init__(number_of_variables=10, number_of_objectives=2, number_of_constraints=0,
                                       dtype=4, ltype=21, ptype=21)
 
@@ -467,7 +464,7 @@ 
    Source code for jmetal.problem.multiobjective.lz09
    [docs]class LZ09_F9(LZ09):
 
-    def __init__(self):
+    def __init__(self):
         super(LZ09_F9, self).__init__(number_of_variables=30, number_of_objectives=2, number_of_constraints=0,
                                       dtype=1, ltype=22, ptype=22)
 
diff --git a/docs/_modules/jmetal/problem/multiobjective/unconstrained.html b/docs/_modules/jmetal/problem/multiobjective/unconstrained.html
index 87361a76..6478e39a 100644
--- a/docs/_modules/jmetal/problem/multiobjective/unconstrained.html
+++ b/docs/_modules/jmetal/problem/multiobjective/unconstrained.html
@@ -24,7 +24,6 @@
     
     
     
-    
     
     
   
@@ -74,8 +73,6 @@ 
    Table Of Contents
    
 
  • Single-objective algorithms
    • 
 
  • Operators
    • 
 
  • Problems
    • 
-
  • Contributing
    • 
-
  • About
    • 
 
 
     
@@ -113,10 +110,10 @@ 
    Table Of Contents
    
             
   
    Source code for jmetal.problem.multiobjective.unconstrained
     import random
-from math import sqrt, exp, pow, sin
+from math import sqrt, exp, pow, sin
 
-from jmetal.core.problem import FloatProblem, BinaryProblem
-from jmetal.core.solution import FloatSolution, BinarySolution
+from jmetal.core.problem import FloatProblem, BinaryProblem
+from jmetal.core.solution import FloatSolution, BinarySolution
 
 """
 .. module:: constrained
@@ -130,7 +127,7 @@ 
    Source code for jmetal.problem.multiobjective.unconstrained
    [docs]class Kursawe(FloatProblem):
     """ Class representing problem Kursawe. """
 
-    def __init__(self, number_of_variables: int = 3):
+    def __init__(self, number_of_variables: int = 3):
         super(Kursawe, self).__init__()
         self.number_of_objectives = 2
         self.number_of_variables = number_of_variables
@@ -165,7 +162,7 @@ 
    Source code for jmetal.problem.multiobjective.unconstrained
    [docs]class Fonseca(FloatProblem):
 
-    def __init__(self):
+    def __init__(self):
         super(Fonseca, self).__init__()
         self.number_of_variables = 3
         self.number_of_objectives = 2
@@ -193,7 +190,7 @@ 
    Source code for jmetal.problem.multiobjective.unconstrained
    [docs]class Schaffer(FloatProblem):
 
-    def __init__(self):
+    def __init__(self):
         super(Schaffer, self).__init__()
         self.number_of_variables = 1
         self.number_of_objectives = 2
@@ -222,7 +219,7 @@ 
    Source code for jmetal.problem.multiobjective.unconstrained
    [docs]class Viennet2(FloatProblem):
 
-    def __init__(self):
+    def __init__(self):
         super(Viennet2, self).__init__()
         self.number_of_variables = 2
         self.number_of_objectives = 3
@@ -257,7 +254,7 @@ 
    Source code for jmetal.problem.multiobjective.unconstrained
    [docs]class SubsetSum(BinaryProblem):
 
-    def __init__(self, C: int, W: list):
+    def __init__(self, C: int, W: list):
         """ The goal is to find a subset S of W whose elements sum is closest to (without exceeding) C.
 
         :param C: Large integer.
@@ -308,7 +305,7 @@ 
    Source code for jmetal.problem.multiobjective.unconstrained
    [docs]class OneZeroMax(BinaryProblem):
 
-    def __init__(self, number_of_bits: int = 256):
+    def __init__(self, number_of_bits: int = 256):
         super(OneZeroMax, self).__init__()
         self.number_of_bits = number_of_bits
         self.number_of_objectives = 2
diff --git a/docs/_modules/jmetal/problem/multiobjective/zdt.html b/docs/_modules/jmetal/problem/multiobjective/zdt.html
index 8f4d14c8..15aaca8f 100644
--- a/docs/_modules/jmetal/problem/multiobjective/zdt.html
+++ b/docs/_modules/jmetal/problem/multiobjective/zdt.html
@@ -24,7 +24,6 @@
     
     
     
-    
     
     
   
@@ -74,8 +73,6 @@ 
    Table Of Contents
    
 
  • Single-objective algorithms
    • 
 
  • Operators
    • 
 
  • Problems
    • 
-
  • Contributing
    • 
-
  • About
    • 
 
 
     
@@ -112,10 +109,10 @@ 
    Table Of Contents
    
           
    
             
   
    Source code for jmetal.problem.multiobjective.zdt
    -from math import sqrt, pow, sin, pi, cos
+from math import sqrt, pow, sin, pi, cos
 
-from jmetal.core.problem import FloatProblem
-from jmetal.core.solution import FloatSolution
+from jmetal.core.problem import FloatProblem
+from jmetal.core.solution import FloatSolution
 
 """
 .. module:: ZDT
@@ -133,7 +130,7 @@ 
    Source code for jmetal.problem.multiobjective.zdt
        .. note:: Continuous problem having a convex Pareto front
     """
 
-    def __init__(self, number_of_variables: int=30):
+    def __init__(self, number_of_variables: int=30):
         """ :param number_of_variables: Number of decision variables of the problem.
         """
         super(ZDT1, self).__init__()
@@ -204,7 +201,7 @@ 
    Source code for jmetal.problem.multiobjective.zdt
        .. note:: Continuous multi-modal problem having a convex Pareto front
     """
 
-    def __init__(self, number_of_variables: int=10):
+    def __init__(self, number_of_variables: int=10):
         """ :param number_of_variables: Number of decision variables of the problem.
         """
         super(ZDT4, self).__init__(number_of_variables=number_of_variables)
@@ -237,7 +234,7 @@ 
    Source code for jmetal.problem.multiobjective.zdt
        .. note:: Continuous problem having a non-convex Pareto front
     """
 
-    def __init__(self, number_of_variables: int=10):
+    def __init__(self, number_of_variables: int=10):
         """ :param number_of_variables: Number of decision variables of the problem.
         """
         super(ZDT6, self).__init__(number_of_variables=number_of_variables)
diff --git a/docs/_modules/jmetal/problem/singleobjective/knapsack.html b/docs/_modules/jmetal/problem/singleobjective/knapsack.html
index 2de933ed..5c2e2c33 100644
--- a/docs/_modules/jmetal/problem/singleobjective/knapsack.html
+++ b/docs/_modules/jmetal/problem/singleobjective/knapsack.html
@@ -24,7 +24,6 @@
     
     
     
-    
     
     
   
@@ -74,8 +73,6 @@ 
    Table Of Contents
    
 
  • Single-objective algorithms
    • 
 
  • Operators
    • 
 
  • Problems
    • 
-
  • Contributing
    • 
-
  • About
    • 
 
 
     
@@ -116,8 +113,8 @@ 
    Source code for jmetal.problem.singleobjective.knapsack
    import numpy as np
 
-from jmetal.core.problem import BinaryProblem
-from jmetal.core.solution import BinarySolution
+from jmetal.core.problem import BinaryProblem
+from jmetal.core.solution import BinarySolution
 
 """
 .. module:: knapsack
@@ -131,7 +128,7 @@ 
    Source code for jmetal.problem.singleobjective.knapsack
    [docs]class Knapsack(BinaryProblem):
     """ Class representing Knapsack Problem. """
 
-    def __init__(self, number_of_items: int = 50, capacity: float = 1000, weights: list = None,
+    def __init__(self, number_of_items: int = 50, capacity: float = 1000, weights: list = None,
                  profits: list = None, from_file: bool = False, filename: str = None):
         super(Knapsack, self).__init__()
 
diff --git a/docs/_modules/jmetal/problem/singleobjective/tsp.html b/docs/_modules/jmetal/problem/singleobjective/tsp.html
index 5a859817..e5b743d4 100644
--- a/docs/_modules/jmetal/problem/singleobjective/tsp.html
+++ b/docs/_modules/jmetal/problem/singleobjective/tsp.html
@@ -24,7 +24,6 @@
     
     
     
-    
     
     
   
@@ -74,8 +73,6 @@ 
    Table Of Contents
    
 
  • Single-objective algorithms
    • 
 
  • Operators
    • 
 
  • Problems
    • 
-
  • Contributing
    • 
-
  • About
    • 
 
 
     
@@ -116,8 +113,8 @@ 
    Source code for jmetal.problem.singleobjective.tsp
    import random
 import re
 
-from jmetal.core.problem import PermutationProblem
-from jmetal.core.solution import PermutationSolution
+from jmetal.core.problem import PermutationProblem
+from jmetal.core.solution import PermutationSolution
 
 """
 .. module:: TSP
@@ -131,7 +128,7 @@ 
    Source code for jmetal.problem.singleobjective.tsp
    [docs]class TSP(PermutationProblem):
     """ Class representing TSP Problem. """
 
-    def __init__(self, instance: str = None):
+    def __init__(self, instance: str = None):
         super(TSP, self).__init__()
 
         distance_matrix, number_of_cities = self.__read_from_file(instance)
diff --git a/docs/_modules/jmetal/problem/singleobjective/unconstrained.html b/docs/_modules/jmetal/problem/singleobjective/unconstrained.html
index 9b3247ec..6bd29fa3 100644
--- a/docs/_modules/jmetal/problem/singleobjective/unconstrained.html
+++ b/docs/_modules/jmetal/problem/singleobjective/unconstrained.html
@@ -24,7 +24,6 @@
     
     
     
-    
     
     
   
@@ -74,8 +73,6 @@ 
    Table Of Contents
    
 
  • Single-objective algorithms
    • 
 
  • Operators
    • 
 
  • Problems
    • 
-
  • Contributing
    • 
-
  • About
    • 
 
 
     
@@ -115,8 +112,8 @@ 
    Source code for jmetal.problem.singleobjective.unconstrained
    import math
 import random
 
-from jmetal.core.problem import BinaryProblem, FloatProblem
-from jmetal.core.solution import BinarySolution, FloatSolution
+from jmetal.core.problem import BinaryProblem, FloatProblem
+from jmetal.core.solution import BinarySolution, FloatSolution
 
 """
 .. module:: unconstrained
@@ -129,7 +126,7 @@ 
    Source code for jmetal.problem.singleobjective.unconstrained
    [docs]class OneMax(BinaryProblem):
 
-    def __init__(self, number_of_bits: int = 256):
+    def __init__(self, number_of_bits: int = 256):
         super(OneMax, self).__init__()
         self.number_of_bits = number_of_bits
         self.number_of_objectives = 1
@@ -161,7 +158,7 @@ 
    Source code for jmetal.problem.singleobjective.unconstrained
    [docs]class Sphere(FloatProblem):
 
-    def __init__(self, number_of_variables: int = 10):
+    def __init__(self, number_of_variables: int = 10):
         super(Sphere, self).__init__()
         self.number_of_objectives = 1
         self.number_of_variables = number_of_variables
@@ -191,7 +188,7 @@ 
    Source code for jmetal.problem.singleobjective.unconstrained
    [docs]class Rastrigin(FloatProblem):
 
-    def __init__(self, number_of_variables: int = 10):
+    def __init__(self, number_of_variables: int = 10):
         super(Rastrigin, self).__init__()
         self.number_of_objectives = 1
         self.number_of_variables = number_of_variables
@@ -224,7 +221,7 @@ 
    Source code for jmetal.problem.singleobjective.unconstrained
    [docs]class SubsetSum(BinaryProblem):
 
-    def __init__(self, C: int, W: list):
+    def __init__(self, C: int, W: list):
         """ The goal is to find a subset S of W whose elements sum is closest to (without exceeding) C.
 
         :param C: Large integer.
diff --git a/docs/_modules/jmetal/util/evaluator.html b/docs/_modules/jmetal/util/evaluator.html
index 95ea657e..663228af 100644
--- a/docs/_modules/jmetal/util/evaluator.html
+++ b/docs/_modules/jmetal/util/evaluator.html
@@ -24,7 +24,6 @@
     
     
     
-    
     
     
   
@@ -74,8 +73,6 @@ 
    Table Of Contents
    
 
  • Single-objective algorithms
    • 
 
  • Operators
    • 
 
  • Problems
    • 
-
  • Contributing
    • 
-
  • About
    • 
 
 
     
@@ -113,9 +110,9 @@ 
    Table Of Contents
    
             
   
    Source code for jmetal.util.evaluator
     import functools
-from abc import ABC, abstractmethod
-from multiprocessing.pool import ThreadPool, Pool
-from typing import TypeVar, List, Generic
+from abc import ABC, abstractmethod
+from multiprocessing.pool import ThreadPool, Pool
+from typing import TypeVar, List, Generic
 
 try:
     import dask
@@ -123,11 +120,11 @@ 
    Source code for jmetal.util.evaluator
         pass
 
 try:
-    from pyspark import SparkConf, SparkContext
+    from pyspark import SparkConf, SparkContext
 except ImportError:
     pass
 
-from jmetal.core.problem import Problem
+from jmetal.core.problem import Problem
 
 S = TypeVar('S')
 
@@ -154,7 +151,7 @@ 
    Source code for jmetal.util.evaluator
     
 
    [docs]class MapEvaluator(Evaluator[S]):
 
-    def __init__(self, processes: int = None):
+    def __init__(self, processes: int = None):
         self.pool = ThreadPool(processes)
 
 
    [docs]    def evaluate(self, solution_list: List[S], problem: Problem) -> List[S]:
@@ -164,7 +161,7 @@ 
    Source code for jmetal.util.evaluator
     
 
 
    [docs]class MultiprocessEvaluator(Evaluator[S]):
-    def __init__(self, processes: int = None):
+    def __init__(self, processes: int = None):
         super().__init__()
         self.pool = Pool(processes)
 
@@ -173,8 +170,8 @@ 
    Source code for jmetal.util.evaluator
     
 
 
    [docs]class SparkEvaluator(Evaluator[S]):
-    def __init__(self, processes: int = 8):
-        self.spark_conf = SparkConf().setAppName("jmetalpy").setMaster(f"local[{processes}]")
+    def __init__(self, processes: int = 8):
+        self.spark_conf = SparkConf().setAppName("jmetalpy").setMaster(f"local[{processes}]")
         self.spark_context = SparkContext(conf=self.spark_conf)
 
         logger = self.spark_context._jvm.org.apache.log4j
@@ -194,7 +191,7 @@ 
    Source code for jmetal.util.evaluator
     
 
 
    [docs]class DaskEvaluator(Evaluator[S]):
-    def __init__(self, scheduler='processes'):
+    def __init__(self, scheduler='processes'):
         self.scheduler = scheduler
 
 
    [docs]    def evaluate(self, solution_list: List[S], problem: Problem) -> List[S]:
diff --git a/docs/_modules/jmetal/util/observer.html b/docs/_modules/jmetal/util/observer.html
index a593769e..db3e6e0d 100644
--- a/docs/_modules/jmetal/util/observer.html
+++ b/docs/_modules/jmetal/util/observer.html
@@ -24,7 +24,6 @@
     
     
     
-    
     
     
   
@@ -74,8 +73,6 @@ 
    Table Of Contents
    
 
  • Single-objective algorithms
    • 
 
  • Operators
    • 
 
  • Problems
    • 
-
  • Contributing
    • 
-
  • About
    • 
 
 
     
@@ -114,16 +111,16 @@ 
    Table Of Contents
    
   
    Source code for jmetal.util.observer
     import logging
 import os
-from pathlib import Path
-from typing import List, TypeVar
+from pathlib import Path
+from typing import List, TypeVar
 
-from tqdm import tqdm
+from tqdm import tqdm
 
-from jmetal.core.observer import Observer
-from jmetal.core.problem import DynamicProblem
-from jmetal.core.quality_indicator import InvertedGenerationalDistance
-from jmetal.lab.visualization import StreamingPlot, Plot
-from jmetal.util.solution import print_function_values_to_file
+from jmetal.core.observer import Observer
+from jmetal.core.problem import DynamicProblem
+from jmetal.core.quality_indicator import InvertedGenerationalDistance
+from jmetal.lab.visualization import StreamingPlot, Plot
+from jmetal.util.solution import print_function_values_to_file
 
 S = TypeVar('S')
 
@@ -140,7 +137,7 @@ 
    Source code for jmetal.util.observer
     
 
    [docs]class ProgressBarObserver(Observer):
 
-    def __init__(self, max: int) -> None:
+    def __init__(self, max: int) -> None:
         """ Show a smart progress meter with the number of evaluations and computing time.
 
         :param max: Number of expected iterations.
@@ -164,7 +161,7 @@ 
    Source code for jmetal.util.observer
     
 
    [docs]class BasicObserver(Observer):
 
-    def __init__(self, frequency: float = 1.0) -> None:
+    def __init__(self, frequency: float = 1.0) -> None:
         """ Show the number of evaluations, best fitness and computing time.
 
         :param frequency: Display frequency. """
@@ -190,7 +187,7 @@ 
    Source code for jmetal.util.observer
     
 
    [docs]class PrintObjectivesObserver(Observer):
 
-    def __init__(self, frequency: float = 1.0) -> None:
+    def __init__(self, frequency: float = 1.0) -> None:
         """ Show the number of evaluations, best fitness and computing time.
 
         :param frequency: Display frequency. """
@@ -215,7 +212,7 @@ 
    Source code for jmetal.util.observer
     
 
    [docs]class WriteFrontToFileObserver(Observer):
 
-    def __init__(self, output_directory: str) -> None:
+    def __init__(self, output_directory: str) -> None:
         """ Write function values of the front into files.
 
         :param output_directory: Output directory. Each front will be saved on a file `FUN.x`. """
@@ -248,7 +245,7 @@ 
    Source code for jmetal.util.observer
     
 
    [docs]class PlotFrontToFileObserver(Observer):
 
-    def __init__(self, output_directory: str, step: int = 100, **kwargs) -> None:
+    def __init__(self, output_directory: str, step: int = 100, **kwargs) -> None:
         """ Plot and save Pareto front approximations into files.
 
         :param output_directory: Output directory.
@@ -289,19 +286,19 @@ 
    Source code for jmetal.util.observer
                                 self.fronts += solutions
                             self.plot_front.plot([self.fronts],
                                                  label=problem.get_name(),
-                                                 filename=f'{self.directory}/front-{evaluations}')
+                                                 filename=f'{self.directory}/front-{evaluations}')
                         self.counter += 1
                         self.last_front = solutions
                 else:
                     self.plot_front.plot([solutions],
-                                         label=f'{evaluations} evaluations',
-                                         filename=f'{self.directory}/front-{evaluations}')
+                                         label=f'{evaluations} evaluations',
+                                         filename=f'{self.directory}/front-{evaluations}')
                     self.counter += 1
    
 
 
 
    [docs]class VisualizerObserver(Observer):
 
-    def __init__(self,
+    def __init__(self,
                  reference_front: List[S] = None,
                  reference_point: list = None,
                  display_frequency: float = 1.0) -> None:
diff --git a/docs/_sources/index.rst.txt b/docs/_sources/index.rst.txt
index 2281df58..a2aea3e4 100644
--- a/docs/_sources/index.rst.txt
+++ b/docs/_sources/index.rst.txt
@@ -30,16 +30,16 @@ Via pip:
 
 .. code-block:: console
 
-    $ pip install jmetalpy
+    $ pip install jmetalpy  # or "jmetalpy[distributed]"
 
 .. note:: Alternatively, you can use one of these instead:
 
     .. code-block:: console
 
-        $ pip install "jmetalpy[core]"  # Install core components of the framework
+        $ pip install "jmetalpy[core]"  # Install core components of the framework (equivalent to `pip install jmetalpy`)
         $ pip install "jmetalpy[docs]"  # Install requirements for building docs
         $ pip install "jmetalpy[distributed]"  # Install requirements for parallel/distributed computing
-        $ pip install "jmetalpy[complete]"  # Install all dependencies (equivalent to `pip install jmetalpy`)
+        $ pip install "jmetalpy[complete]"  # Install all dependencies
 
 Via source code:
 
diff --git a/docs/_static/basic.css b/docs/_static/basic.css
index ea6972d5..b04360d6 100644
--- a/docs/_static/basic.css
+++ b/docs/_static/basic.css
@@ -672,6 +672,10 @@ div.code-block-caption + div > div.highlight > pre {
     margin-top: 0;
 }
 
+div.doctest > div.highlight span.gp {  /* gp: Generic.Prompt */
+    user-select: none;
+}
+
 div.code-block-caption span.caption-number {
     padding: 0.1em 0.3em;
     font-style: italic;
diff --git a/docs/_static/documentation_options.js b/docs/_static/documentation_options.js
index a4d2c925..61206e63 100644
--- a/docs/_static/documentation_options.js
+++ b/docs/_static/documentation_options.js
@@ -3,6 +3,7 @@ var DOCUMENTATION_OPTIONS = {
     VERSION: '1.5.3',
     LANGUAGE: 'None',
     COLLAPSE_INDEX: false,
+    BUILDER: 'html',
     FILE_SUFFIX: '.html',
     HAS_SOURCE: true,
     SOURCELINK_SUFFIX: '.txt',
diff --git a/docs/_static/searchtools.js b/docs/_static/searchtools.js
index 6031f991..ad845872 100644
--- a/docs/_static/searchtools.js
+++ b/docs/_static/searchtools.js
@@ -245,7 +245,7 @@ var Search = {
       if (results.length) {
         var item = results.pop();
         var listItem = $('
  • ');
-        if (DOCUMENTATION_OPTIONS.FILE_SUFFIX === '') {
+        if (DOCUMENTATION_OPTIONS.BUILDER === 'dirhtml') {
           // dirhtml builder
           var dirname = item[0] + '/';
           if (dirname.match(/\/index\/$/)) {
@@ -424,7 +424,7 @@ var Search = {
         for (j = 0; j < _files.length; j++) {
           file = _files[j];
           if (!(file in scoreMap))
-            scoreMap[file] = {}
+            scoreMap[file] = {};
           scoreMap[file][word] = o.score;
         }
       });
@@ -432,7 +432,7 @@ var Search = {
       // create the mapping
       for (j = 0; j < files.length; j++) {
         file = files[j];
-        if (file in fileMap)
+        if (file in fileMap && fileMap[file].indexOf(word) === -1)
           fileMap[file].push(word);
         else
           fileMap[file] = [word];
diff --git a/docs/about.rst b/docs/about.rst
new file mode 100644
index 00000000..d5029674
--- /dev/null
+++ b/docs/about.rst
@@ -0,0 +1,28 @@
+About
+==============
+
+jMetalPy is being developed by `Antonio J. Nebro `_, associate professor at the University of Málaga, and `Antonio Benítez-Hidalgo `_, PhD Student in Computer Science at the University of Málaga.
+
+Cite us
+------------------------
+
+.. code-block:: LaTeX
+
+   @article{BENITEZHIDALGO2019100598,
+      title = "jMetalPy: A Python framework for multi-objective optimization with metaheuristics",
+      journal = "Swarm and Evolutionary Computation",
+      pages = "100598",
+      year = "2019",
+      issn = "2210-6502",
+      doi = "https://doi.org/10.1016/j.swevo.2019.100598",
+      url = "http://www.sciencedirect.com/science/article/pii/S2210650219301397",
+      author = "Antonio Benítez-Hidalgo and Antonio J. Nebro and José García-Nieto and Izaskun Oregi and Javier Del Ser",
+      keywords = "Multi-objective optimization, Metaheuristics, Software framework, Python, Statistical analysis, Visualization",
+   }
+
+References
+------------------------
+
+1. J.J. Durillo, A.J. Nebro jMetal: a Java Framework for Multi-Objective Optimization. Advances in Engineering Software 42 (2011) 760-771.
+2. A.J. Nebro, J.J. Durillo, M. Vergne Redesigning the jMetal Multi-Objective Optimization Framework. GECCO (Companion) 2015, pp: 1093-1100. July 2015.
+3. Nebro A.J. et al. (2018) Extending the Speed-Constrained Multi-objective PSO (SMPSO) with Reference Point Based Preference Articulation. In: Auger A., Fonseca C., Lourenço N., Machado P., Paquete L., Whitley D. (eds) Parallel Problem Solving from Nature – PPSN XV. PPSN 2018. Lecture Notes in Computer Science, vol 11101. Springer, Cham
\ No newline at end of file
diff --git a/docs/api/algorithm/multiobjective/ea.html b/docs/api/algorithm/multiobjective/ea.html
index 375565da..e43d4443 100644
--- a/docs/api/algorithm/multiobjective/ea.html
+++ b/docs/api/algorithm/multiobjective/ea.html
@@ -24,7 +24,6 @@
     
     
     
-    
     
     
     
@@ -86,8 +85,6 @@ 
    Table Of Contents
    
 
  • Single-objective algorithms
    • 
 
  • Operators
    • 
 
  • Problems
    • 
-
  • Contributing
    • 
-
  • About
    • 
 
 
     
diff --git a/docs/api/algorithm/multiobjective/eas/gde3.html b/docs/api/algorithm/multiobjective/eas/gde3.html
index d5ccd511..bafdf1eb 100644
--- a/docs/api/algorithm/multiobjective/eas/gde3.html
+++ b/docs/api/algorithm/multiobjective/eas/gde3.html
@@ -24,7 +24,6 @@
     
     
     
-    
     
     
     
@@ -87,8 +86,6 @@ 
    Table Of Contents
    
 
  • Single-objective algorithms
    • 
 
  • Operators
    • 
 
  • Problems
    • 
-
  • Contributing
    • 
-
  • About
    • 
 
 
     
@@ -350,9 +347,9 @@ 
    Example
    -from jmetal.algorithm.multiobjective.gde3 import GDE3
-from jmetal.problem import ZDT1
-from jmetal.util.termination_criterion import StoppingByEvaluations
+from jmetal.algorithm.multiobjective.gde3 import GDE3
+from jmetal.problem import ZDT1
+from jmetal.util.termination_criterion import StoppingByEvaluations
 
 problem = ZDT1()
 
@@ -377,8 +374,8 @@ 
    Example
    -from jmetal.lab.visualization.plotting import Plot
-from jmetal.util.solution import get_non_dominated_solutions
+from jmetal.lab.visualization.plotting import Plot
+from jmetal.util.solution import get_non_dominated_solutions
 
 front = get_non_dominated_solutions(solutions)
 
diff --git a/docs/api/algorithm/multiobjective/eas/gde3_dynamic.html b/docs/api/algorithm/multiobjective/eas/gde3_dynamic.html
index 787beb7e..f0e9315a 100644
--- a/docs/api/algorithm/multiobjective/eas/gde3_dynamic.html
+++ b/docs/api/algorithm/multiobjective/eas/gde3_dynamic.html
@@ -24,7 +24,6 @@
     
     
     
-    
     
     
   
@@ -73,8 +72,6 @@ 
    Table Of Contents
    
 
  • Single-objective algorithms
    • 
 
  • Operators
    • 
 
  • Problems
    • 
-
  • Contributing
    • 
-
  • About
    • 
 
 
     
@@ -332,11 +329,11 @@ 
    Example
    -from jmetal.algorithm.multiobjective.gde3 import DynamicGDE3
-from jmetal.problem.multiobjective.fda import FDA2
-from jmetal.util.observable import TimeCounter
-from jmetal.util.observer import PlotFrontToFileObserver, WriteFrontToFileObserver
-from jmetal.util.termination_criterion import StoppingByEvaluations
+from jmetal.algorithm.multiobjective.gde3 import DynamicGDE3
+from jmetal.problem.multiobjective.fda import FDA2
+from jmetal.util.observable import TimeCounter
+from jmetal.util.observer import PlotFrontToFileObserver, WriteFrontToFileObserver
+from jmetal.util.termination_criterion import StoppingByEvaluations
 
 problem = FDA2()
 
diff --git a/docs/api/algorithm/multiobjective/eas/gde3_preference.html b/docs/api/algorithm/multiobjective/eas/gde3_preference.html
index bdc04b5b..387081d7 100644
--- a/docs/api/algorithm/multiobjective/eas/gde3_preference.html
+++ b/docs/api/algorithm/multiobjective/eas/gde3_preference.html
@@ -24,7 +24,6 @@
     
     
     
-    
     
     
   
@@ -73,8 +72,6 @@ 
    Table Of Contents
    
 
  • Single-objective algorithms
    • 
 
  • Operators
    • 
 
  • Problems
    • 
-
  • Contributing
    • 
-
  • About
    • 
 
 
     
@@ -332,10 +329,10 @@ 
    Example
    -from jmetal.algorithm.multiobjective.gde3 import GDE3
-from jmetal.problem import ZDT2
-from jmetal.util.comparator import GDominanceComparator
-from jmetal.util.termination_criterion import StoppingByEvaluations
+from jmetal.algorithm.multiobjective.gde3 import GDE3
+from jmetal.problem import ZDT2
+from jmetal.util.comparator import GDominanceComparator
+from jmetal.util.termination_criterion import StoppingByEvaluations
 
 problem = ZDT2()
 
@@ -362,8 +359,8 @@ 
    Example
    -from jmetal.lab.visualization.plotting import Plot
-from jmetal.util.solution import get_non_dominated_solutions
+from jmetal.lab.visualization.plotting import Plot
+from jmetal.util.solution import get_non_dominated_solutions
 
 front = get_non_dominated_solutions(solutions)
 
diff --git a/docs/api/algorithm/multiobjective/eas/hype.html b/docs/api/algorithm/multiobjective/eas/hype.html
index c773292e..031b297c 100644
--- a/docs/api/algorithm/multiobjective/eas/hype.html
+++ b/docs/api/algorithm/multiobjective/eas/hype.html
@@ -24,7 +24,6 @@
     
     
     
-    
     
     
     
@@ -87,8 +86,6 @@ 
    Table Of Contents
    
 
  • Single-objective algorithms
    • 
 
  • Operators
    • 
 
  • Problems
    • 
-
  • Contributing
    • 
-
  • About
    • 
 
 
     
@@ -350,11 +347,11 @@ 
    Example
    -from jmetal.algorithm.multiobjective.hype import HYPE
-from jmetal.core.solution import FloatSolution
-from jmetal.operator import SBXCrossover, PolynomialMutation
-from jmetal.problem import ZDT1
-from jmetal.util.termination_criterion import StoppingByEvaluations
+from jmetal.algorithm.multiobjective.hype import HYPE
+from jmetal.core.solution import FloatSolution
+from jmetal.operator import SBXCrossover, PolynomialMutation
+from jmetal.problem import ZDT1
+from jmetal.util.termination_criterion import StoppingByEvaluations
 
 problem = ZDT1()
 
@@ -382,8 +379,8 @@ 
    Example
    -from jmetal.lab.visualization.plotting import Plot
-from jmetal.util.solution import get_non_dominated_solutions
+from jmetal.lab.visualization.plotting import Plot
+from jmetal.util.solution import get_non_dominated_solutions
 
 front = get_non_dominated_solutions(solutions)
 
diff --git a/docs/api/algorithm/multiobjective/eas/ibea.html b/docs/api/algorithm/multiobjective/eas/ibea.html
index f3aed7d2..8ba4ea3a 100644
--- a/docs/api/algorithm/multiobjective/eas/ibea.html
+++ b/docs/api/algorithm/multiobjective/eas/ibea.html
@@ -24,7 +24,6 @@
     
     
     
-    
     
     
     
@@ -87,8 +86,6 @@ 
    Table Of Contents
    
 
  • Single-objective algorithms
    • 
 
  • Operators
    • 
 
  • Problems
    • 
-
  • Contributing
    • 
-
  • About
    • 
 
 
     
@@ -350,10 +347,10 @@ 
    Example
    -from jmetal.algorithm.multiobjective.ibea import IBEA
-from jmetal.operator import SBXCrossover, PolynomialMutation
-from jmetal.problem import ZDT1
-from jmetal.util.termination_criterion import StoppingByEvaluations
+from jmetal.algorithm.multiobjective.ibea import IBEA
+from jmetal.operator import SBXCrossover, PolynomialMutation
+from jmetal.problem import ZDT1
+from jmetal.util.termination_criterion import StoppingByEvaluations
 
 problem = ZDT1()
 
@@ -380,7 +377,7 @@ 
    Example
    -from jmetal.lab.visualization.plotting import Plot
+from jmetal.lab.visualization.plotting import Plot
 
 plot_front = Plot(plot_title='Pareto front approximation', axis_labels=['x', 'y'])
 plot_front.plot(front, label='IBEA-ZDT1')
diff --git a/docs/api/algorithm/multiobjective/eas/mocell.html b/docs/api/algorithm/multiobjective/eas/mocell.html
index fe150058..cb72e771 100644
--- a/docs/api/algorithm/multiobjective/eas/mocell.html
+++ b/docs/api/algorithm/multiobjective/eas/mocell.html
@@ -24,7 +24,6 @@
     
     
     
-    
     
     
     
@@ -87,8 +86,6 @@ 
    Table Of Contents
    
 
  • Single-objective algorithms
    • 
 
  • Operators
    • 
 
  • Problems
    • 
-
  • Contributing
    • 
-
  • About
    • 
 
 
     
@@ -350,12 +347,12 @@ 
    Example
    -from jmetal.algorithm.multiobjective.mocell import MOCell
-from jmetal.operator import SBXCrossover, PolynomialMutation
-from jmetal.problem import ZDT4
-from jmetal.util.archive import CrowdingDistanceArchive
-from jmetal.util.neighborhood import C9
-from jmetal.util.termination_criterion import StoppingByEvaluations
+from jmetal.algorithm.multiobjective.mocell import MOCell
+from jmetal.operator import SBXCrossover, PolynomialMutation
+from jmetal.problem import ZDT4
+from jmetal.util.archive import CrowdingDistanceArchive
+from jmetal.util.neighborhood import C9
+from jmetal.util.termination_criterion import StoppingByEvaluations
 
 problem = ZDT4()
 
@@ -382,7 +379,7 @@ 
    Example
    -from jmetal.lab.visualization.plotting import Plot
+from jmetal.lab.visualization.plotting import Plot
 
 plot_front = Plot(plot_title='Pareto front approximation', axis_labels=['x', 'y'])
 plot_front.plot(front, label='MOCell-ZDT4')
diff --git a/docs/api/algorithm/multiobjective/eas/moead.html b/docs/api/algorithm/multiobjective/eas/moead.html
index 1d0daeda..1c602ed6 100644
--- a/docs/api/algorithm/multiobjective/eas/moead.html
+++ b/docs/api/algorithm/multiobjective/eas/moead.html
@@ -24,7 +24,6 @@
     
     
     
-    
     
     
     
@@ -87,8 +86,6 @@ 
    Table Of Contents
    
 
  • Single-objective algorithms
    • 
 
  • Operators
    • 
 
  • Problems
    • 
-
  • Contributing
    • 
-
  • About
    • 
 
 
     
@@ -350,11 +347,11 @@ 
    Example
    -from jmetal.algorithm.multiobjective.moead import MOEAD
-from jmetal.operator import PolynomialMutation, DifferentialEvolutionCrossover
-from jmetal.problem import LZ09_F2
-from jmetal.util.aggregative_function import Tschebycheff
-from jmetal.util.termination_criterion import StoppingByEvaluations
+from jmetal.algorithm.multiobjective.moead import MOEAD
+from jmetal.operator import PolynomialMutation, DifferentialEvolutionCrossover
+from jmetal.problem import LZ09_F2
+from jmetal.util.aggregative_function import Tschebycheff
+from jmetal.util.termination_criterion import StoppingByEvaluations
 
 problem = LZ09_F2()
 
@@ -384,8 +381,8 @@ 
    Example
    -from jmetal.lab.visualization.plotting import Plot
-from jmetal.util.solution import get_non_dominated_solutions
+from jmetal.lab.visualization.plotting import Plot
+from jmetal.util.solution import get_non_dominated_solutions
 
 front = get_non_dominated_solutions(solutions)
 
diff --git a/docs/api/algorithm/multiobjective/eas/nsgaii.html b/docs/api/algorithm/multiobjective/eas/nsgaii.html
index 4b8c9cc5..dd967e12 100644
--- a/docs/api/algorithm/multiobjective/eas/nsgaii.html
+++ b/docs/api/algorithm/multiobjective/eas/nsgaii.html
@@ -24,7 +24,6 @@
     
     
     
-    
     
     
     
@@ -87,8 +86,6 @@ 
    Table Of Contents
    
 
  • Single-objective algorithms
    • 
 
  • Operators
    • 
 
  • Problems
    • 
-
  • Contributing
    • 
-
  • About
    • 
 
 
     
@@ -350,10 +347,10 @@ 
    Example
    -from jmetal.algorithm.multiobjective.nsgaii import NSGAII
-from jmetal.operator import SBXCrossover, PolynomialMutation
-from jmetal.problem import ZDT1
-from jmetal.util.termination_criterion import StoppingByEvaluations
+from jmetal.algorithm.multiobjective.nsgaii import NSGAII
+from jmetal.operator import SBXCrossover, PolynomialMutation
+from jmetal.problem import ZDT1
+from jmetal.util.termination_criterion import StoppingByEvaluations
 
 problem = ZDT1()
 
@@ -379,8 +376,8 @@ 
    Example
    -from jmetal.lab.visualization.plotting import Plot
-from jmetal.util.solution import get_non_dominated_solutions
+from jmetal.lab.visualization.plotting import Plot
+from jmetal.util.solution import get_non_dominated_solutions
 
 front = get_non_dominated_solutions(solutions)
 
diff --git a/docs/api/algorithm/multiobjective/eas/nsgaii_distributed.html b/docs/api/algorithm/multiobjective/eas/nsgaii_distributed.html
index 86ad5b74..0ee04d63 100644
--- a/docs/api/algorithm/multiobjective/eas/nsgaii_distributed.html
+++ b/docs/api/algorithm/multiobjective/eas/nsgaii_distributed.html
@@ -24,7 +24,6 @@
     
     
     
-    
     
     
   
@@ -73,8 +72,6 @@ 
    Table Of Contents
    
 
  • Single-objective algorithms
    • 
 
  • Operators
    • 
 
  • Problems
    • 
-
  • Contributing
    • 
-
  • About
    • 
 
 
     
@@ -332,15 +329,15 @@ 
    Example
    -from math import sqrt
+from math import sqrt
 
-from jmetal.core.problem import FloatProblem
-from jmetal.core.solution import FloatSolution
+from jmetal.core.problem import FloatProblem
+from jmetal.core.solution import FloatSolution
 
 
 class ZDT1Modified(FloatProblem):
 
-    def __init__(self, number_of_variables: int=30):
+    def __init__(self, number_of_variables: int=30):
         """ :param number_of_variables: Number of decision variables of the problem.
         """
         super(ZDT1Modified, self).__init__()
@@ -390,12 +387,12 @@ 
    Example
    -from dask.distributed import Client
-from distributed import LocalCluster
+from dask.distributed import Client
+from distributed import LocalCluster
 
-from jmetal.algorithm.multiobjective.nsgaii import DistributedNSGAII
-from jmetal.operator import PolynomialMutation, SBXCrossover
-from jmetal.util.termination_criterion import StoppingByEvaluations
+from jmetal.algorithm.multiobjective.nsgaii import DistributedNSGAII
+from jmetal.operator import PolynomialMutation, SBXCrossover
+from jmetal.util.termination_criterion import StoppingByEvaluations
 
 # ZDT1 version including a loop for increasing the computing time
 #  of the evaluation functions
@@ -405,7 +402,7 @@ 
    Exampleclient = Client(LocalCluster(n_workers=24))
 
 ncores = sum(client.ncores().values())
-print(f'{ncores} cores available')
+print(f'{ncores} cores available')
 
 # creates the algorithm
 max_evaluations = 25000
@@ -431,8 +428,8 @@ 
    Example
    -from jmetal.lab.visualization.plotting import Plot
-from jmetal.util.solution import get_non_dominated_solutions
+from jmetal.lab.visualization.plotting import Plot
+from jmetal.util.solution import get_non_dominated_solutions
 
 front = get_non_dominated_solutions(solutions)
 
diff --git a/docs/api/algorithm/multiobjective/eas/nsgaii_dynamic.html b/docs/api/algorithm/multiobjective/eas/nsgaii_dynamic.html
index 982c131f..a2b9427f 100644
--- a/docs/api/algorithm/multiobjective/eas/nsgaii_dynamic.html
+++ b/docs/api/algorithm/multiobjective/eas/nsgaii_dynamic.html
@@ -24,7 +24,6 @@
     
     
     
-    
     
     
   
@@ -73,8 +72,6 @@ 
    Table Of Contents
    
 
  • Single-objective algorithms
    • 
 
  • Operators
    • 
 
  • Problems
    • 
-
  • Contributing
    • 
-
  • About
    • 
 
 
     
@@ -332,12 +329,12 @@ 
    Example
    -from jmetal.algorithm.multiobjective.nsgaii import DynamicNSGAII
-from jmetal.operator import PolynomialMutation, SBXCrossover
-from jmetal.problem.multiobjective.fda import FDA2
-from jmetal.util.observable import TimeCounter
-from jmetal.util.observer import PlotFrontToFileObserver, WriteFrontToFileObserver
-from jmetal.util.termination_criterion import StoppingByEvaluations
+from jmetal.algorithm.multiobjective.nsgaii import DynamicNSGAII
+from jmetal.operator import PolynomialMutation, SBXCrossover
+from jmetal.problem.multiobjective.fda import FDA2
+from jmetal.util.observable import TimeCounter
+from jmetal.util.observer import PlotFrontToFileObserver, WriteFrontToFileObserver
+from jmetal.util.termination_criterion import StoppingByEvaluations
 
 problem = FDA2()
 
diff --git a/docs/api/algorithm/multiobjective/eas/nsgaii_preference.html b/docs/api/algorithm/multiobjective/eas/nsgaii_preference.html
index 07df542c..2c17d5d3 100644
--- a/docs/api/algorithm/multiobjective/eas/nsgaii_preference.html
+++ b/docs/api/algorithm/multiobjective/eas/nsgaii_preference.html
@@ -24,7 +24,6 @@
     
     
     
-    
     
     
   
@@ -73,8 +72,6 @@ 
    Table Of Contents
    
 
  • Single-objective algorithms
    • 
 
  • Operators
    • 
 
  • Problems
    • 
-
  • Contributing
    • 
-
  • About
    • 
 
 
     
@@ -332,11 +329,11 @@ 
    Example
    -from jmetal.algorithm.multiobjective.nsgaii import NSGAII
-from jmetal.operator import SBXCrossover, PolynomialMutation
-from jmetal.problem import ZDT2
-from jmetal.util.comparator import GDominanceComparator
-from jmetal.util.termination_criterion import StoppingByEvaluations
+from jmetal.algorithm.multiobjective.nsgaii import NSGAII
+from jmetal.operator import SBXCrossover, PolynomialMutation
+from jmetal.problem import ZDT2
+from jmetal.util.comparator import GDominanceComparator
+from jmetal.util.termination_criterion import StoppingByEvaluations
 
 problem = ZDT2()
 
@@ -364,8 +361,8 @@ 
    Example
    -from jmetal.lab.visualization.plotting import Plot
-from jmetal.util.solution import get_non_dominated_solutions
+from jmetal.lab.visualization.plotting import Plot
+from jmetal.util.solution import get_non_dominated_solutions
 
 front = get_non_dominated_solutions(solutions)
 
diff --git a/docs/api/algorithm/multiobjective/eas/nsgaiii.html b/docs/api/algorithm/multiobjective/eas/nsgaiii.html
index b6c2e5a6..c7113082 100644
--- a/docs/api/algorithm/multiobjective/eas/nsgaiii.html
+++ b/docs/api/algorithm/multiobjective/eas/nsgaiii.html
@@ -24,7 +24,6 @@
     
     
     
-    
     
     
     
@@ -87,8 +86,6 @@ 
    Table Of Contents
    
 
  • Single-objective algorithms
    • 
 
  • Operators
    • 
 
  • Problems
    • 
-
  • Contributing
    • 
-
  • About
    • 
 
 
     
@@ -144,35 +141,6 @@ 
    Contents
    
 
    NSGA-III
    
 
    
 
    API
    
-
    
-
    
-class jmetal.algorithm.multiobjective.nsgaiii.NSGAIII(reference_directions, problem: jmetal.core.problem.Problem, mutation: jmetal.core.operator.Mutation, crossover: jmetal.core.operator.Crossover, population_size: int = None, selection: jmetal.core.operator.Selection = <jmetal.operator.selection.BinaryTournamentSelection object>, termination_criterion: jmetal.util.termination_criterion.TerminationCriterion = <jmetal.util.termination_criterion.StoppingByEvaluations object>, population_generator: jmetal.util.generator.Generator = <jmetal.util.generator.RandomGenerator object>, population_evaluator: jmetal.util.evaluator.Evaluator = <jmetal.util.evaluator.SequentialEvaluator object>, dominance_comparator: jmetal.util.comparator.Comparator = <jmetal.util.comparator.DominanceComparator object>)[source]
    
-
    Bases: jmetal.algorithm.multiobjective.nsgaii.NSGAII
    
-
    
-
    
-get_name() → str[source]
    
-
    
-
-
    
-
    
-get_result()[source]
    
-
    Return only non dominated solutions.
    
-
    
-
-
    
-
    
-replacement(population: List[S], offspring_population: List[S]) → List[S][source]
    
-
    Implements NSGA-III environmental selection based on reference points as described in:
    
-
      
-
    • Deb, K., & Jain, H. (2014). An Evolutionary Many-Objective Optimization
-Algorithm Using Reference-Point-Based Nondominated Sorting Approach,
-Part I: Solving Problems With Box Constraints. IEEE Transactions on
-Evolutionary Computation, 18(4), 577–601. doi:10.1109/TEVC.2013.2281535.
      • 
-
    
-
    
-
-
    
-
 
    
 
    
 
diff --git a/docs/api/algorithm/multiobjective/eas/spea2.html b/docs/api/algorithm/multiobjective/eas/spea2.html
index 2c7494bc..4a388cb7 100644
--- a/docs/api/algorithm/multiobjective/eas/spea2.html
+++ b/docs/api/algorithm/multiobjective/eas/spea2.html
@@ -24,7 +24,6 @@
     
     
     
-    
     
     
     
@@ -87,8 +86,6 @@ 
    Table Of Contents
    
 
  • Single-objective algorithms
    • 
 
  • Operators
    • 
 
  • Problems
    • 
-
  • Contributing
    • 
-
  • About
    • 
 
 
     
@@ -350,10 +347,10 @@ 
    Example
    -from jmetal.algorithm.multiobjective.spea2 import SPEA2
-from jmetal.operator import SBXCrossover, PolynomialMutation
-from jmetal.problem import ZDT1
-from jmetal.util.termination_criterion import StoppingByEvaluations
+from jmetal.algorithm.multiobjective.spea2 import SPEA2
+from jmetal.operator import SBXCrossover, PolynomialMutation
+from jmetal.problem import ZDT1
+from jmetal.util.termination_criterion import StoppingByEvaluations
 
 problem = ZDT1()
 
@@ -379,8 +376,8 @@ 
    Example
    -from jmetal.lab.visualization.plotting import Plot
-from jmetal.util.solution import get_non_dominated_solutions
+from jmetal.lab.visualization.plotting import Plot
+from jmetal.util.solution import get_non_dominated_solutions
 
 front = get_non_dominated_solutions(solutions)
 
diff --git a/docs/api/algorithm/multiobjective/pso.html b/docs/api/algorithm/multiobjective/pso.html
index 45e028ea..13d22aa3 100644
--- a/docs/api/algorithm/multiobjective/pso.html
+++ b/docs/api/algorithm/multiobjective/pso.html
@@ -24,7 +24,6 @@
     
     
     
-    
     
     
     
@@ -86,8 +85,6 @@ 
    Table Of Contents
    
 
  • Single-objective algorithms
    • 
 
  • Operators
    • 
 
  • Problems
    • 
-
  • Contributing
    • 
-
  • About
    • 
 
 
     
diff --git a/docs/api/algorithm/multiobjective/psos/omopso.html b/docs/api/algorithm/multiobjective/psos/omopso.html
index e58f7de4..f3b7e057 100644
--- a/docs/api/algorithm/multiobjective/psos/omopso.html
+++ b/docs/api/algorithm/multiobjective/psos/omopso.html
@@ -24,7 +24,6 @@
     
     
     
-    
     
     
     
@@ -87,8 +86,6 @@ 
    Table Of Contents
    
 
  • Single-objective algorithms
    • 
 
  • Operators
    • 
 
  • Problems
    • 
-
  • Contributing
    • 
-
  • About
    • 
 
 
     
@@ -350,12 +347,12 @@ 
    Example
    -from jmetal.algorithm.multiobjective.omopso import OMOPSO
-from jmetal.operator import UniformMutation
-from jmetal.operator.mutation import NonUniformMutation
-from jmetal.problem import ZDT1
-from jmetal.util.archive import CrowdingDistanceArchive
-from jmetal.util.termination_criterion import StoppingByEvaluations
+from jmetal.algorithm.multiobjective.omopso import OMOPSO
+from jmetal.operator import UniformMutation
+from jmetal.operator.mutation import NonUniformMutation
+from jmetal.problem import ZDT1
+from jmetal.util.archive import CrowdingDistanceArchive
+from jmetal.util.termination_criterion import StoppingByEvaluations
 
 problem = ZDT1()
 
@@ -385,8 +382,8 @@ 
    Example
    -from jmetal.lab.visualization.plotting import Plot
-from jmetal.util.solution import get_non_dominated_solutions
+from jmetal.lab.visualization.plotting import Plot
+from jmetal.util.solution import get_non_dominated_solutions
 
 front = get_non_dominated_solutions(solutions)
 
diff --git a/docs/api/algorithm/multiobjective/psos/smpso.html b/docs/api/algorithm/multiobjective/psos/smpso.html
index 8a46d82b..d7fa6d2c 100644
--- a/docs/api/algorithm/multiobjective/psos/smpso.html
+++ b/docs/api/algorithm/multiobjective/psos/smpso.html
@@ -24,7 +24,6 @@
     
     
     
-    
     
     
     
@@ -87,8 +86,6 @@ 
    Table Of Contents
    
 
  • Single-objective algorithms
    • 
 
  • Operators
    • 
 
  • Problems
    • 
-
  • Contributing
    • 
-
  • About
    • 
 
 
     
@@ -350,11 +347,11 @@ 
    Example
    -from jmetal.algorithm.multiobjective.smpso import SMPSO
-from jmetal.operator import PolynomialMutation
-from jmetal.problem import ZDT4
-from jmetal.util.archive import CrowdingDistanceArchive
-from jmetal.util.termination_criterion import StoppingByEvaluations
+from jmetal.algorithm.multiobjective.smpso import SMPSO
+from jmetal.operator import PolynomialMutation
+from jmetal.problem import ZDT4
+from jmetal.util.archive import CrowdingDistanceArchive
+from jmetal.util.termination_criterion import StoppingByEvaluations
 
 problem = ZDT4()
 
@@ -378,8 +375,8 @@ 
    Example
    -from jmetal.lab.visualization.plotting import Plot
-from jmetal.util.solution import get_non_dominated_solutions
+from jmetal.lab.visualization.plotting import Plot
+from jmetal.util.solution import get_non_dominated_solutions
 
 front = get_non_dominated_solutions(solutions)
 
diff --git a/docs/api/algorithm/multiobjective/psos/smpso_dynamic.html b/docs/api/algorithm/multiobjective/psos/smpso_dynamic.html
index 2bef588e..263340e5 100644
--- a/docs/api/algorithm/multiobjective/psos/smpso_dynamic.html
+++ b/docs/api/algorithm/multiobjective/psos/smpso_dynamic.html
@@ -24,7 +24,6 @@
     
     
     
-    
     
     
   
@@ -73,8 +72,6 @@ 
    Table Of Contents
    
 
  • Single-objective algorithms
    • 
 
  • Operators
    • 
 
  • Problems
    • 
-
  • Contributing
    • 
-
  • About
    • 
 
 
     
@@ -332,13 +329,13 @@ 
    Example
    -from jmetal.algorithm.multiobjective.smpso import DynamicSMPSO
-from jmetal.operator import PolynomialMutation
-from jmetal.problem.multiobjective.fda import FDA2
-from jmetal.util.archive import CrowdingDistanceArchive
-from jmetal.util.observable import TimeCounter
-from jmetal.util.observer import PlotFrontToFileObserver, WriteFrontToFileObserver
-from jmetal.util.termination_criterion import StoppingByEvaluations
+from jmetal.algorithm.multiobjective.smpso import DynamicSMPSO
+from jmetal.operator import PolynomialMutation
+from jmetal.problem.multiobjective.fda import FDA2
+from jmetal.util.archive import CrowdingDistanceArchive
+from jmetal.util.observable import TimeCounter
+from jmetal.util.observer import PlotFrontToFileObserver, WriteFrontToFileObserver
+from jmetal.util.termination_criterion import StoppingByEvaluations
 
 problem = FDA2()
 
diff --git a/docs/api/algorithm/multiobjective/psos/smpso_preference.html b/docs/api/algorithm/multiobjective/psos/smpso_preference.html
index 5a098933..30d42dfa 100644
--- a/docs/api/algorithm/multiobjective/psos/smpso_preference.html
+++ b/docs/api/algorithm/multiobjective/psos/smpso_preference.html
@@ -24,7 +24,6 @@
     
     
     
-    
     
     
   
@@ -73,8 +72,6 @@ 
    Table Of Contents
    
 
  • Single-objective algorithms
    • 
 
  • Operators
    • 
 
  • Problems
    • 
-
  • Contributing
    • 
-
  • About
    • 
 
 
     
@@ -332,11 +329,11 @@ 
    Example
    -from jmetal.algorithm.multiobjective.smpso import SMPSORP
-from jmetal.operator import PolynomialMutation
-from jmetal.problem import ZDT4
-from jmetal.util.archive import CrowdingDistanceArchiveWithReferencePoint
-from jmetal.util.termination_criterion import StoppingByEvaluations
+from jmetal.algorithm.multiobjective.smpso import SMPSORP
+from jmetal.operator import PolynomialMutation
+from jmetal.problem import ZDT4
+from jmetal.util.archive import CrowdingDistanceArchiveWithReferencePoint
+from jmetal.util.termination_criterion import StoppingByEvaluations
 
 problem = ZDT4()
 
@@ -379,8 +376,8 @@ 
    Example
    -from jmetal.lab.visualization.plotting import Plot
-from jmetal.util.solution import get_non_dominated_solutions
+from jmetal.lab.visualization.plotting import Plot
+from jmetal.util.solution import get_non_dominated_solutions
 
 front = get_non_dominated_solutions(solutions)
 
diff --git a/docs/api/algorithm/singleobjective/evolution.strategy.html b/docs/api/algorithm/singleobjective/evolution.strategy.html
index 653a5d59..6c54d8e9 100644
--- a/docs/api/algorithm/singleobjective/evolution.strategy.html
+++ b/docs/api/algorithm/singleobjective/evolution.strategy.html
@@ -24,7 +24,6 @@
     
     
     
-    
     
     
     
@@ -88,8 +87,6 @@ 
    Table Of Contents
    
 
 
  • Operators
    • 
 
  • Problems
    • 
-
  • Contributing
    • 
-
  • About
    • 
 
 
     
diff --git a/docs/api/algorithm/singleobjective/genetic.algorithm.html b/docs/api/algorithm/singleobjective/genetic.algorithm.html
index 77a42545..1f961a5f 100644
--- a/docs/api/algorithm/singleobjective/genetic.algorithm.html
+++ b/docs/api/algorithm/singleobjective/genetic.algorithm.html
@@ -24,7 +24,6 @@
     
     
     
-    
     
     
     
@@ -88,8 +87,6 @@ 
    Table Of Contents
    
 
 
  • Operators
    • 
 
  • Problems
    • 
-
  • Contributing
    • 
-
  • About
    • 
 
 
     
diff --git a/docs/api/algorithm/singleobjective/local.search.html b/docs/api/algorithm/singleobjective/local.search.html
index 95efe04f..e68ef879 100644
--- a/docs/api/algorithm/singleobjective/local.search.html
+++ b/docs/api/algorithm/singleobjective/local.search.html
@@ -24,7 +24,6 @@
     
     
     
-    
     
     
     
@@ -88,8 +87,6 @@ 
    Table Of Contents
    
 
 
  • Operators
    • 
 
  • Problems
    • 
-
  • Contributing
    • 
-
  • About
    • 
 
 
     
diff --git a/docs/api/algorithm/singleobjective/simulated.annealing.html b/docs/api/algorithm/singleobjective/simulated.annealing.html
index 3c9c0ca3..6a616e28 100644
--- a/docs/api/algorithm/singleobjective/simulated.annealing.html
+++ b/docs/api/algorithm/singleobjective/simulated.annealing.html
@@ -24,7 +24,6 @@
     
     
     
-    
     
     
     
@@ -88,8 +87,6 @@ 
    Table Of Contents
    
 
 
  • Operators
    • 
 
  • Problems
    • 
-
  • Contributing
    • 
-
  • About
    • 
 
 
     
diff --git a/docs/api/jmetal.lab.statistical_test.html b/docs/api/jmetal.lab.statistical_test.html
index b32f42b4..d06ea703 100644
--- a/docs/api/jmetal.lab.statistical_test.html
+++ b/docs/api/jmetal.lab.statistical_test.html
@@ -24,7 +24,6 @@
     
     
     
-    
     
     
   
@@ -73,8 +72,6 @@ 
    Table Of Contents
    
 
  • Single-objective algorithms
    • 
 
  • Operators
    • 
 
  • Problems
    • 
-
  • Contributing
    • 
-
  • About
    • 
 
 
     
@@ -89,7 +86,7 @@ 
    Contents
    
 
  • Submodules
    • 
 
  • jmetal.lab.statistical_test.apv_procedures module
    • 
 
  • jmetal.lab.statistical_test.bayesian module
    • 
-
  • jmetal.lab.statistical_test.critical_distance module
    • 
+
  • jmetal.lab.statistical_test.critical_distance module
    • 
 
  • jmetal.lab.statistical_test.functions module
    • 
 
  • Module contents
    • 
 
@@ -262,8 +259,29 @@ 
    Submodules
-
    jmetal.lab.statistical_test.critical_distance module
    
+
    
+
    jmetal.lab.statistical_test.critical_distance module
    
+
    
+
    
+jmetal.lab.statistical_test.critical_distance.CDplot(results, alpha: float = 0.05, higher_is_better: bool = False, alg_names: list = None, output_filename: str = 'cdplot.eps')[source]
    
+
    CDgraph plots the critical difference graph show in Janez Demsar’s 2006 work:
+* Statistical Comparisons of Classifiers over Multiple Data Sets.
+:param results: A 2-D array containing results from each algorithm. Each row of ‘results’ represents an algorithm, and each column a dataset.
+:param alpha: {0.1, 0.999}. Significance level for the critical difference.
+:param alg_names: Names of the tested algorithms.
    
+
    
+
+
    
+
    
+jmetal.lab.statistical_test.critical_distance.NemenyiCD(alpha: float, num_alg, num_dataset)[source]
    
+
    Computes Nemenyi’s critical difference:
+* CD = q_alpha * sqrt(num_alg*(num_alg + 1)/(6*num_prob))
+where q_alpha is the critical value, of the Studentized range statistic divided by sqrt(2).
+:param alpha: {0.1, 0.999}. Significance level.
+:param num_alg: number of tested algorithms.
+:param num_dataset: Number of problems/datasets where the algorithms have been tested.
    
+
    
+
 
    
 
    
 
    jmetal.lab.statistical_test.functions module
    
diff --git a/docs/api/operator/crossover.html b/docs/api/operator/crossover.html
index 7a06aa8f..fd162339 100644
--- a/docs/api/operator/crossover.html
+++ b/docs/api/operator/crossover.html
@@ -24,7 +24,6 @@
     
     
     
-    
     
     
     
@@ -87,8 +86,6 @@ 
    Table Of Contents
    
 
 
 
  • Problems
    • 
-
  • Contributing
    • 
-
  • About
    • 
 
 
     
diff --git a/docs/api/operator/mutation.html b/docs/api/operator/mutation.html
index 9659ca43..715d3a86 100644
--- a/docs/api/operator/mutation.html
+++ b/docs/api/operator/mutation.html
@@ -24,7 +24,6 @@
     
     
     
-    
     
     
     
@@ -87,8 +86,6 @@ 
    Table Of Contents
    
 
 
 
  • Problems
    • 
-
  • Contributing
    • 
-
  • About
    • 
 
 
     
diff --git a/docs/api/operator/selection.html b/docs/api/operator/selection.html
index 949a58cf..d8f9db07 100644
--- a/docs/api/operator/selection.html
+++ b/docs/api/operator/selection.html
@@ -24,7 +24,6 @@
     
     
     
-    
     
     
     
@@ -87,8 +86,6 @@ 
    Table Of Contents
    
 
 
 
  • Problems
    • 
-
  • Contributing
    • 
-
  • About
    • 
 
 
     
diff --git a/docs/api/problem/multiobjective.html b/docs/api/problem/multiobjective.html
index 76f0f141..68ba9b68 100644
--- a/docs/api/problem/multiobjective.html
+++ b/docs/api/problem/multiobjective.html
@@ -24,7 +24,6 @@
     
     
     
-    
     
     
     
@@ -86,8 +85,6 @@ 
    Table Of Contents
    
 
  • Single-objective problems
    • 
 
 
-
  • Contributing
    • 
-
  • About
    • 
 
 
     
diff --git a/docs/api/problem/singleobjective.html b/docs/api/problem/singleobjective.html
index 2cbdb06e..2a266833 100644
--- a/docs/api/problem/singleobjective.html
+++ b/docs/api/problem/singleobjective.html
@@ -24,10 +24,8 @@
     
     
     
-    
     
     
-    
     
   
    
@@ -42,9 +40,6 @@ 
    Navigation
    
         
  • 
           modules |
    • 
-        
  • 
-          next |
    • 
         
  • 
           previous |
    • 
@@ -86,8 +81,6 @@ 
    Table Of Contents
    
 
  • Single-objective problems
    • 
 
 
-
  • Contributing
    • 
-
  • About
    • 
 
 
     
@@ -350,9 +343,6 @@ 
    Single-objective problems← Multi-objective problems
       
    
     
-      
    
-        Contributing →
-      
    
     
    
     
    
   
@@ -368,9 +358,6 @@ 
    Navigation
    
         
  • 
           modules |
    • 
-        
  • 
-          next |
    • 
         
  • 
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diff --git a/docs/contributing.rst b/docs/contributing.rst
new file mode 100644
index 00000000..5ddc89f8
--- /dev/null
+++ b/docs/contributing.rst
@@ -0,0 +1,203 @@
+Contributing
+================================================
+
+Contributions to the jMetalPy project are welcome.
+Please, take into account the following guidelines (all developers should follow these guidelines):
+
+Git WorkFlow
+-------------------------------
+
+We have a set of branches on the remote Git server.
+Some branches are temporary, and others are constant throughout the life of the repository.
+
+* Branches always present in the repository:
+    * *master*: You have the latest released to production, receive merges from the develop branch, or merge from a *hotfix* branch (emergency).
+        * Do I have to put a TAG when doing a merge from develop to master? yes
+        * Do I have to put a TAG when doing a merge from a hotfix branch to master? yes
+        * After merge from a hotfix to master, do I have to merge from master to develop? yes
+    * *develop*: It is considered the "Next Release", receives merges from branches of each developer, either corrections (*fix*) or new features (*feature*).
+
+* Temporary branches:
+    * *feature/\\-\*: When we are doing a development, we create a local branch with the prefix "feature/", then only if there is a task id, we indicate it and we add a hyphen. The following we indicate a description according to the functionality that we are developing. The words are separated by hyphens.
+        * Where does this branch emerge? This branch always emerge from the develop branch
+        * When I finish the development in my feature branch, which branch to merge into?: You always merge feature branch into develop branch
+
+    * *fix/\\-\*: When we are making a correction, we create a local branch with the prefix "fix/", then only if there is a task id, we indicate it and we add a hyphen. The following we indicate a description according to the functionality that we are correcting. The words are separated by hyphens.
+        * Where does this branch emerge? This branch always emerge from the develop branch
+        * When I finish the correction in my fix branch, which branch to merge into?: You always merge feature branch into develop branch
+
+    * *hotfix/\\-\*: When we are correcting an emergency incidence in production, we create a local branch with the prefix "hotfix/", then only if there is a task id, we indicate it and we add a hyphen. The following we indicate a description according to the functionality that we are correcting. The words are separated by hyphens.
+        * Where does this branch emerge?: This branch always emerge from the master branch
+        * When I finish the correction in my hotfix branch, which branch to merge into?: This branch always emerge from the master and develop branch
+
+* Steps to follow when you are creating or going to work on a branch of any kind (feature / fix / hotfix):
+    1. After you create your branch (feature / fix / hotfix) locally, upload it to the remote Git server. The integration system will verify your code from the outset.
+    2. Each time you commit, as much as possible, you send a push to the server. Each push will trigger the automated launch of the tests, etc.
+    3. Once the development is finished, having done a push to the remote Git server, and that the test phase has passed without problem, you create an `pull request `_.
+
+.. note::  Do not forget to remove your branch (feature / fix / hotfix) once the merge has been made.
+
+Some useful Git commands:
+
+* git fetch --prune: Cleaning branches removed and bringing new branches
+
+PEP8!
+-------------------------------
+
+It is really important to follow some standards when a team develops an application. If all team members format the code in the same format, then it is much easier to read the code. PEP8 is Python's style guide. It's a set of rules for how to format your Python code.
+
+Some style rules:
+
+* Package and module names: Modules should have short, **all-lowercase** names. Underscores can be used in the module name if it improves readability. Python packages should also have short, **all-lowercase** names, although the use of underscores is discouraged. In Python, a module is a file with the suffix '.py'.
+* Class names: Class names should normally use the **CapWords** convention.
+* Method names and instance variables: **Lowercase with words separated by underscores** as necessary to improve readability.
+
+There are many more style standards in PEP8 so, please, refer to `PEP8 documentation `_
+. The most appropriate is to use an IDE that has support for PEP8. For example, `PyCharm `_.
+
+Object-oriented programming
+-------------------------------
+
+**Object-oriented programming should be the single programming paradigm used**. Avoiding as far as possible, imperative and functional programming.
+
+.. image:: contributing/python_poo_programming.png
+
+.. image:: contributing/python_functional_programming.png
+
+.. image:: contributing/python_imperative_programming.png
+
+In classes, we directly access the attributes, which are usually defined as public.
+
+.. image:: contributing/without_getter_setter.png
+
+Only when we want to **implement additional logic in the accesses to the attributes** we define getter/setter methods, but **always by using the *property*** annotation or the ***property*** function:
+
+.. image:: contributing/property_annotation.png
+
+.. image:: contributing/property_functional.png
+
+By using ***property***, we continue to access the attributes directly:
+
+.. image:: contributing/good_access.png
+
+Do not use getter/setter methods without the *property* annotation or the *property* function:
+
+.. image:: contributing/with_getter_setter.png
+
+Since this way of accessing the attribute is not commonly used in Python:
+
+.. image:: contributing/bad_access.png
+
+Structure
+-------------------------------
+
+Python is not Java. In Java you cannot, by design, have more than one class in a file. In Python, you can do it.
+
+In Python, **it is appropriate to group several classes into a single .py file. For that reason, the .py files are called modules.**
+
+Python 3.6
+-------------------------------
+
+We **always** define types in the parameters of the arguments and the return value:
+
+.. image:: contributing/types_in_methods.png
+
+We can define abstract classes (ABCs) in Python:
+
+.. image:: contributing/abstract.png
+
+In the case that we want to define an **interface** class, it is done in the same way. We just have to define all the methods of the class as abstract.
+
+Example of use of generic types:
+
+.. image:: contributing/generic_types.png
+
+In the code below, the IDE displays a **warning**, since although the 2nd parameter is a float type, which is a type allowed in the definition of the generic type X, it is not of the same type as the first, since the first 2 parameters must be of the same generic type (S):
+
+.. image:: contributing/instance_with_generic_types1_wearning.png
+
+In the code below, the IDE displays a **warning**, since the 2nd parameter is a type not allowed in the definition of the generic type ( *TypeVar('S', int, float)* ):
+
+.. image:: contributing/instance_with_generic_types2_wearning.png
+
+When the class inherits from *Generic[...]*, the **class is defined as generic**. In this way we can indicate the types that will have the values of the generic types, when using the class as type. Look at the *add_car()* method of the *Parking* class.
+
+.. note:: The generic classes inherit from abc.ABCMeta, so they are abstract classes and **abstract methods can be used**.
+
+.. image:: contributing/generic_class1.png
+
+.. image:: contributing/generic_class2.png
+
+In the code below, the IDE displays a **warning** in the call to the *add_car()* method when adding the car, since the 3rd parameter of the init must be a *str* type, as defined in the *add_car()* method of the *Parking* class.
+
+.. image:: contributing/instance_with_generic_class_wearning.png
+
+When inheriting from generic classes, some type variables could be fixed:
+
+.. image:: contributing/generic_types_fixed.png
+
+Example of inheritance from non-generic class to generic class:
+
+.. image:: contributing/inheritance_non_generic_to_generic.png
+
+Example of inheritance from generic class to another generic class:
+
+.. image:: contributing/inheritance_generic_to_generic.png
+
+Create automatic documentation files with Sphinx
+--------------------------------------------------
+
+First, you need to know how to correctly document your code. It is **important** to follow these simple rules in order to automatically create good documentation for the project.
+
+When you create a new module file (testDoc.py in this example), you should mention it using this format:
+
+.. code-block:: python
+
+  """
+  .. module:: testDoc
+     :platform: Unix, Windows
+     :synopsis: A useful module indeed.
+
+  .. moduleauthor:: Andrew Carter 
+  """
+
+  class testDoc(object):
+      """We use this as a public class example class.
+
+      This class is ruled by the very trendy important method :func:`public_fn_with_sphinxy_docstring`.
+
+      .. note::
+         An example of intersphinx is this: you **cannot** use :mod:`pickle` on this class.
+      """
+
+      def __init__(self):
+          pass
+
+This code snippet generates the following documentation:
+
+.. image:: contributing/class_header.png
+
+Now, you can document your methods using the following sintax:
+
+.. code-block:: python
+
+   def public_fn_with_sphinxy_docstring(self, name: str, state: bool = False) -> int:
+       """This function does something.
+
+       :param name: The name to use.
+       :type name: str.
+       :param state: Current state to be in.
+       :type state: bool.
+       :returns:  int -- the return code.
+       :raises: AttributeError, KeyError
+       """
+       return 0
+
+   def public_fn_without_docstring(self):
+       return True
+
+And the produced output doc will be:
+
+.. image:: contributing/method_way_sphinx.png
+
+As you may notice, if you don't use any docstring, the method documentation will be empty.
\ No newline at end of file
diff --git a/docs/genindex.html b/docs/genindex.html
index f7dfcb29..c38c136c 100644
--- a/docs/genindex.html
+++ b/docs/genindex.html
@@ -25,7 +25,6 @@
     
     
     
-    
     
     
   
@@ -74,8 +73,6 @@ 
    Table Of Contents
    
 
  • Single-objective algorithms
    • 
 
  • Operators
    • 
 
  • Problems
    • 
-
  • Contributing
    • 
-
  • About
    • 
 
 
     
@@ -184,6 +181,8 @@ 
    B
    
 
    C
    
 
    
   
       
  • EvolutionStrategy (class in jmetal.algorithm.singleobjective.evolution_strategy)
 
    • 
-      
  • execute() (jmetal.operator.crossover.CXCrossover method)
+      
  • execute() (jmetal.lab.experiment.Job method)
 
       
    • 
+      
  • Experiment (class in jmetal.lab.experiment)
+
    • 
       
  • export_to_div() (jmetal.lab.visualization.interactive.InteractivePlot method)
 
    • 
       
  • export_to_html() (jmetal.lab.visualization.interactive.InteractivePlot method)
@@ -571,6 +574,8 @@ 
    G
    
       
  • GDE3 (class in jmetal.algorithm.multiobjective.gde3)
 
    • 
       
  • generate_permutation_of_neighbors() (jmetal.algorithm.multiobjective.moead.MOEAD method)
+
    • 
+      
  • generate_summary_from_experiment() (in module jmetal.lab.experiment)
 
    • 
       
  • GeneticAlgorithm (class in jmetal.algorithm.singleobjective.genetic_algorithm)
 
    • 
@@ -588,8 +593,6 @@ 
    G
    
         
  • (jmetal.algorithm.multiobjective.nsgaii.DistributedNSGAII method)
 
    • 
         
  • (jmetal.algorithm.multiobjective.nsgaii.NSGAII method)
-
    • 
-        
  • (jmetal.algorithm.multiobjective.nsgaiii.NSGAIII method)
 
    • 
         
  • (jmetal.algorithm.multiobjective.omopso.OMOPSO method)
 
    • 
@@ -830,8 +833,6 @@ 
    G
    
         
  • (jmetal.algorithm.multiobjective.nsgaii.DistributedNSGAII method)
 
    • 
         
  • (jmetal.algorithm.multiobjective.nsgaii.NSGAII method)
-
    • 
-        
  • (jmetal.algorithm.multiobjective.nsgaiii.NSGAIII method)
 
    • 
         
  • (jmetal.algorithm.multiobjective.omopso.OMOPSO method)
 
    • 
@@ -941,12 +942,16 @@ 
    J
    
       
  • jmetal.core.observer (module)
 
    • 
       
  • jmetal.core.problem (module)
+
    • 
+      
  • jmetal.lab.experiment (module)
 
    • 
       
  • jmetal.lab.statistical_test (module)
 
    • 
       
  • jmetal.lab.statistical_test.apv_procedures (module)
 
    • 
       
  • jmetal.lab.statistical_test.bayesian (module)
+
    • 
+      
  • jmetal.lab.statistical_test.critical_distance (module)
 
    • 
       
  • jmetal.lab.statistical_test.functions (module)
 
    • 
@@ -989,6 +994,8 @@ 
    J
    
       
  • jmetal.problem.singleobjective.unconstrained (module)
 
    • 
       
  • jmetal.util.observer (module)
+
    • 
+      
  • Job (class in jmetal.lab.experiment)
 
    • 
       		
 
    
@@ -1098,15 +1105,15 @@ 
    N
    
 
       
  • nemenyi() (in module jmetal.lab.statistical_test.apv_procedures)
 
    • 
-      
  • NonUniformMutation (class in jmetal.operator.mutation)
+      
  • NemenyiCD() (in module jmetal.lab.statistical_test.critical_distance)
 
    • 
-      
  • notify_all() (jmetal.core.observer.Observable method)
+      
  • NonUniformMutation (class in jmetal.operator.mutation)
 
    • 
   
   
 
 
diff --git a/docs/index.html b/docs/index.html
index 257288f4..418e259c 100644
--- a/docs/index.html
+++ b/docs/index.html
@@ -24,7 +24,6 @@
     
     
     
-    
     
     
     
@@ -77,8 +76,6 @@ 
    Table Of Contents
    
 
  • Single-objective algorithms
    • 
 
  • Operators
    • 
 
  • Problems
    • 
-
  • Contributing
    • 
-
  • About
    • 
 
 
     
@@ -155,24 +152,22 @@ 
    ContentSingle-objective algorithms
 
  • Operators
    • 
 
  • Problems
    • 
-
  • Contributing
    • 
-
  • About
    • 
 
 
    
 
    
 
    
 
    Installation steps
    
 
    Via pip:
    
-
    $ pip install jmetalpy
+
    $ pip install jmetalpy  # or "jmetalpy[distributed]"
 
    
 
    
 
    
 
    Note
    
 
    Alternatively, you can use one of these instead:
    
-
    $ pip install "jmetalpy[core]"  # Install core components of the framework
+
    $ pip install "jmetalpy[core]"  # Install core components of the framework (equivalent to `pip install jmetalpy`)
 $ pip install "jmetalpy[docs]"  # Install requirements for building docs
 $ pip install "jmetalpy[distributed]"  # Install requirements for parallel/distributed computing
-$ pip install "jmetalpy[complete]"  # Install all dependencies (equivalent to `pip install jmetalpy`)
+$ pip install "jmetalpy[complete]"  # Install all dependencies
 
    
 
    
 
    
diff --git a/docs/multiobjective.algorithms.html b/docs/multiobjective.algorithms.html
index 0fd8b214..2ee60a0b 100644
--- a/docs/multiobjective.algorithms.html
+++ b/docs/multiobjective.algorithms.html
@@ -24,7 +24,6 @@
     
     
     
-    
     
     
     
@@ -85,8 +84,6 @@ 
    Table Of Contents
    
 
  • Single-objective algorithms
    • 
 
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    • 
 
  • Problems
    • 
-
  • Contributing
    • 
-
  • About
    • 
 
 
     
diff --git a/docs/objects.inv b/docs/objects.inv
index 16f01e0f..aff7d790 100644
Binary files a/docs/objects.inv and b/docs/objects.inv differ
diff --git a/docs/operators.html b/docs/operators.html
index bc0c0a0a..c5cc70d2 100644
--- a/docs/operators.html
+++ b/docs/operators.html
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diff --git a/docs/problems.html b/docs/problems.html
index 83599ff2..0ee4d93e 100644
--- a/docs/problems.html
+++ b/docs/problems.html
@@ -24,7 +24,6 @@
     
     
     
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diff --git a/docs/py-modindex.html b/docs/py-modindex.html
index ad9df54c..7ef219f2 100644
--- a/docs/py-modindex.html
+++ b/docs/py-modindex.html
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@@ -76,8 +75,6 @@ 
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    • 
 
  • Problems
    • 
-
  • Contributing
    • 
-
  • About
    • 
 
 
     
@@ -159,6 +156,11 @@ 
    Python Module Index
    
           
        jmetal.core.problem
        
+     
+       
+          
+       jmetal.lab.experiment
+       
      
        
           
@@ -174,6 +176,11 @@ 
    Python Module Index
    
           
        jmetal.lab.statistical_test.bayesian
        
+     
+       
+          
+       jmetal.lab.statistical_test.critical_distance
+       
      
        
           
diff --git a/docs/search.html b/docs/search.html
index fdfa6734..bc6b585f 100644
--- a/docs/search.html
+++ b/docs/search.html
@@ -26,7 +26,6 @@
     
     
     
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@@ -78,8 +77,6 @@ 
    Table Of Contents
    
 
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    • 
 
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    • 
 
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    • 
-
  • Contributing
    • 
-
  • About
    • 
 
 
     
diff --git a/docs/searchindex.js b/docs/searchindex.js
index c7196a8e..9f93e852 100644
--- a/docs/searchindex.js
+++ b/docs/searchindex.js
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Algorithms","GDE3","Dynamic GDE3","Preference point-based GDE3","HYPE","IBEA","MOCell","MOEA/D","NSGA-II","Distributed NSGA-II","Dynamic NSGA-II","Preference point-based NSGA-II","NSGA-III","SPEA2","PSO Algorithms","OMOPSO","SMPSO","Dynamic SMPSO","SMPSO/RP","Evolution Strategy","Genetic Algorithm","Local Search","Simulated annealing","jmetal.lab.statistical_test package","Crossover","Mutation","Selection","Multi-objective problems","Single-objective problems","jMetalPy: Python version of the jMetal framework","Multi-objective algorithms","Operators","Problems","Single-objective algorithms","Getting started","Evaluate solutions","Experiments","Extending algorithms","Defining new problems","Statistical analysis","Front 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\ No newline at end of file
diff --git a/docs/singleobjective.algorithms.html b/docs/singleobjective.algorithms.html
index 1cf94361..f5da783f 100644
--- a/docs/singleobjective.algorithms.html
+++ b/docs/singleobjective.algorithms.html
@@ -24,7 +24,6 @@
     
     
     
-    
     
     
     
@@ -87,8 +86,6 @@ 
    Table Of Contents
    
 
 
  • Operators
    • 
 
  • Problems
    • 
-
  • Contributing
    • 
-
  • About
    • 
 
 
     
diff --git a/docs/source/api/algorithm/multiobjective/ea.rst b/docs/source/api/algorithm/multiobjective/ea.rst
new file mode 100644
index 00000000..d8706648
--- /dev/null
+++ b/docs/source/api/algorithm/multiobjective/ea.rst
@@ -0,0 +1,15 @@
+Evolutionary Algorithms
+=======================
+
+.. toctree::
+   :maxdepth: 1
+   :caption: Multi-objective evolutionary algorithms:
+
+   eas/gde3
+   eas/hype
+   eas/ibea
+   eas/mocell
+   eas/moead
+   eas/nsgaii
+   eas/nsgaiii
+   eas/spea2
diff --git a/docs/source/api/algorithm/multiobjective/eas/gde3.ipynb b/docs/source/api/algorithm/multiobjective/eas/gde3.ipynb
new file mode 100644
index 00000000..68b89242
--- /dev/null
+++ b/docs/source/api/algorithm/multiobjective/eas/gde3.ipynb
@@ -0,0 +1,152 @@
+{
+ "cells": [
+  {
+   "cell_type": "raw",
+   "metadata": {
+    "raw_mimetype": "text/restructuredtext"
+   },
+   "source": [
+    ".. _nb_gde3:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## GDE3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Example"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "pycharm": {
+     "is_executing": true,
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from jmetal.algorithm.multiobjective.gde3 import GDE3\n",
+    "from jmetal.problem import ZDT1\n",
+    "from jmetal.util.termination_criterion import StoppingByEvaluations\n",
+    "\n",
+    "problem = ZDT1()\n",
+    "\n",
+    "max_evaluations = 25000\n",
+    "\n",
+    "algorithm = GDE3(\n",
+    "    problem=problem,\n",
+    "    population_size=100,\n",
+    "    cr=0.5,\n",
+    "    f=0.5,\n",
+    "    termination_criterion=StoppingByEvaluations(max_evaluations)\n",
+    ")\n",
+    "\n",
+    "algorithm.run()\n",
+    "solutions = algorithm.get_result()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can now visualize the Pareto front approximation:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "pycharm": {
+     "is_executing": true,
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "
    "
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from jmetal.lab.visualization.plotting import Plot\n",
+    "from jmetal.util.solution import get_non_dominated_solutions\n",
+    "\n",
+    "front = get_non_dominated_solutions(solutions)\n",
+    "    \n",
+    "plot_front = Plot(plot_title='Pareto front approximation', axis_labels=['x', 'y'])\n",
+    "plot_front.plot(front, label='GDE3-ZDT1')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### API"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "metadata": {
+    "pycharm": {
+     "is_executing": false,
+     "name": "#%% raw\n"
+    },
+    "raw_mimetype": "text/restructuredtext"
+   },
+   "source": [
+    ".. autoclass:: jmetal.algorithm.multiobjective.gde3.GDE3\n",
+    "   :members:\n",
+    "   :undoc-members:\n",
+    "   :show-inheritance:\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "celltoolbar": "Raw Cell Format",
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  },
+  "pycharm": {
+   "stem_cell": {
+    "cell_type": "raw",
+    "metadata": {
+     "collapsed": false
+    },
+    "source": []
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/docs/source/api/algorithm/multiobjective/eas/gde3_dynamic.ipynb b/docs/source/api/algorithm/multiobjective/eas/gde3_dynamic.ipynb
new file mode 100644
index 00000000..f88f5378
--- /dev/null
+++ b/docs/source/api/algorithm/multiobjective/eas/gde3_dynamic.ipynb
@@ -0,0 +1,118 @@
+{
+ "cells": [
+  {
+   "cell_type": "raw",
+   "metadata": {
+    "raw_mimetype": "text/restructuredtext"
+   },
+   "source": [
+    ".. _nb_gde3_dynamic:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Dynamic GDE3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Example"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "pycharm": {
+     "is_executing": false,
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from jmetal.algorithm.multiobjective.gde3 import DynamicGDE3\n",
+    "from jmetal.problem.multiobjective.fda import FDA2\n",
+    "from jmetal.util.observable import TimeCounter\n",
+    "from jmetal.util.observer import PlotFrontToFileObserver, WriteFrontToFileObserver\n",
+    "from jmetal.util.termination_criterion import StoppingByEvaluations\n",
+    "\n",
+    "problem = FDA2()\n",
+    "\n",
+    "time_counter = TimeCounter(delay=1)\n",
+    "time_counter.observable.register(problem)\n",
+    "time_counter.start()\n",
+    "\n",
+    "algorithm = DynamicGDE3(\n",
+    "    problem=problem,\n",
+    "    population_size=100,\n",
+    "    cr=0.5,\n",
+    "    f=0.5,\n",
+    "    termination_criterion=StoppingByEvaluations(max=500)\n",
+    ")\n",
+    "\n",
+    "algorithm.observable.register(observer=PlotFrontToFileObserver('front_plot'))\n",
+    "algorithm.observable.register(observer=WriteFrontToFileObserver('front_files'))\n",
+    "\n",
+    "algorithm.run()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### API"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "metadata": {
+    "pycharm": {
+     "is_executing": false,
+     "name": "#%% raw\n"
+    },
+    "raw_mimetype": "text/restructuredtext"
+   },
+   "source": [
+    ".. autoclass:: jmetal.algorithm.multiobjective.gde3.DynamicGDE3\n",
+    "   :members:\n",
+    "   :undoc-members:\n",
+    "   :show-inheritance:\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "celltoolbar": "Raw Cell Format",
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  },
+  "pycharm": {
+   "stem_cell": {
+    "cell_type": "raw",
+    "source": [],
+    "metadata": {
+     "collapsed": false
+    }
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
\ No newline at end of file
diff --git a/docs/source/api/algorithm/multiobjective/eas/gde3_preference.ipynb b/docs/source/api/algorithm/multiobjective/eas/gde3_preference.ipynb
new file mode 100644
index 00000000..ff25efe4
--- /dev/null
+++ b/docs/source/api/algorithm/multiobjective/eas/gde3_preference.ipynb
@@ -0,0 +1,151 @@
+{
+ "cells": [
+  {
+   "cell_type": "raw",
+   "metadata": {
+    "raw_mimetype": "text/restructuredtext"
+   },
+   "source": [
+    ".. _nb_gde3_preference:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Preference point-based GDE3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Example"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "pycharm": {
+     "is_executing": false,
+     "name": "#%%\n"
+    },
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "from jmetal.algorithm.multiobjective.gde3 import GDE3\n",
+    "from jmetal.problem import ZDT2\n",
+    "from jmetal.util.comparator import GDominanceComparator\n",
+    "from jmetal.util.termination_criterion import StoppingByEvaluations\n",
+    "\n",
+    "problem = ZDT2()\n",
+    "\n",
+    "max_evaluations = 25000\n",
+    "reference_point = [.5, .5]\n",
+    "\n",
+    "algorithm = GDE3(\n",
+    "    problem=problem,\n",
+    "    population_size=100,\n",
+    "    cr=0.5,\n",
+    "    f=0.5,\n",
+    "    termination_criterion=StoppingByEvaluations(max=max_evaluations),\n",
+    "    dominance_comparator=GDominanceComparator(reference_point)\n",
+    ")\n",
+    "\n",
+    "algorithm.run()\n",
+    "solutions = algorithm.get_result()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can now visualize the Pareto front approximation:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "pycharm": {
+     "is_executing": false,
+     "name": "#%% \n"
+    },
+    "raw_mimetype": "text/restructuredtext"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "
    "
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from jmetal.lab.visualization.plotting import Plot\n",
+    "from jmetal.util.solution import get_non_dominated_solutions\n",
+    "\n",
+    "front = get_non_dominated_solutions(solutions)\n",
+    "    \n",
+    "plot_front = Plot(plot_title='Pareto front approximation', axis_labels=['x', 'y'], reference_point=reference_point)\n",
+    "plot_front.plot(front, label='gGDE3-ZDT2')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### API"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "metadata": {},
+   "source": [
+    ".. autoclass:: jmetal.algorithm.multiobjective.gde3.GDE3\n",
+    "   :members:\n",
+    "   :undoc-members:\n",
+    "   :show-inheritance:\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "celltoolbar": "Raw Cell Format",
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  },
+  "pycharm": {
+   "stem_cell": {
+    "cell_type": "raw",
+    "source": [],
+    "metadata": {
+     "collapsed": false
+    }
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
\ No newline at end of file
diff --git a/docs/source/api/algorithm/multiobjective/eas/hype.ipynb b/docs/source/api/algorithm/multiobjective/eas/hype.ipynb
new file mode 100644
index 00000000..bd86d8a7
--- /dev/null
+++ b/docs/source/api/algorithm/multiobjective/eas/hype.ipynb
@@ -0,0 +1,157 @@
+{
+ "cells": [
+  {
+   "cell_type": "raw",
+   "metadata": {
+    "raw_mimetype": "text/restructuredtext"
+   },
+   "source": [
+    ".. _nb_hype:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## HYPE"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Example"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "pycharm": {
+     "is_executing": false,
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from jmetal.algorithm.multiobjective.hype import HYPE\n",
+    "from jmetal.core.solution import FloatSolution\n",
+    "from jmetal.operator import SBXCrossover, PolynomialMutation\n",
+    "from jmetal.problem import ZDT1\n",
+    "from jmetal.util.termination_criterion import StoppingByEvaluations\n",
+    "\n",
+    "problem = ZDT1()\n",
+    "\n",
+    "reference_point = FloatSolution([0], [1], problem.number_of_objectives, )\n",
+    "reference_point.objectives = [1., 1.]  # Mandatory for HYPE\n",
+    "\n",
+    "algorithm = HYPE(\n",
+    "    problem=problem,\n",
+    "    reference_point=reference_point,\n",
+    "    population_size=100,\n",
+    "    offspring_population_size=100,\n",
+    "    mutation=PolynomialMutation(probability=1.0 / problem.number_of_variables, distribution_index=20),\n",
+    "    crossover=SBXCrossover(probability=1.0, distribution_index=20),\n",
+    "    termination_criterion=StoppingByEvaluations(2500)\n",
+    ")\n",
+    "\n",
+    "algorithm.run()\n",
+    "solutions = algorithm.get_result()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can now visualize the Pareto front approximation:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "pycharm": {
+     "is_executing": false,
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "
    "
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from jmetal.lab.visualization.plotting import Plot\n",
+    "from jmetal.util.solution import get_non_dominated_solutions\n",
+    "\n",
+    "front = get_non_dominated_solutions(solutions)\n",
+    "    \n",
+    "plot_front = Plot(plot_title='Pareto front approximation', axis_labels=['x', 'y'])\n",
+    "plot_front.plot(front, label='HYPE-ZDT1')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### API"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "metadata": {
+    "pycharm": {
+     "is_executing": false,
+     "name": "#%% raw\n"
+    },
+    "raw_mimetype": "text/restructuredtext"
+   },
+   "source": [
+    ".. autoclass:: jmetal.algorithm.multiobjective.hype.HYPE\n",
+    "   :members:\n",
+    "   :undoc-members:\n",
+    "   :show-inheritance:\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "celltoolbar": "Raw Cell Format",
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  },
+  "pycharm": {
+   "stem_cell": {
+    "cell_type": "raw",
+    "metadata": {
+     "collapsed": false
+    },
+    "source": []
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/docs/source/api/algorithm/multiobjective/eas/ibea.ipynb b/docs/source/api/algorithm/multiobjective/eas/ibea.ipynb
new file mode 100644
index 00000000..a90881ca
--- /dev/null
+++ b/docs/source/api/algorithm/multiobjective/eas/ibea.ipynb
@@ -0,0 +1,152 @@
+{
+ "cells": [
+  {
+   "cell_type": "raw",
+   "metadata": {
+    "raw_mimetype": "text/restructuredtext"
+   },
+   "source": [
+    ".. _nb_ibea:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## IBEA"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Example"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "pycharm": {
+     "is_executing": false,
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from jmetal.algorithm.multiobjective.ibea import IBEA\n",
+    "from jmetal.operator import SBXCrossover, PolynomialMutation\n",
+    "from jmetal.problem import ZDT1\n",
+    "from jmetal.util.termination_criterion import StoppingByEvaluations\n",
+    "\n",
+    "problem = ZDT1()\n",
+    "\n",
+    "max_evaluations = 25000\n",
+    "\n",
+    "algorithm = IBEA(\n",
+    "    problem=problem,\n",
+    "    kappa=1.,\n",
+    "    population_size=100,\n",
+    "    offspring_population_size=100,\n",
+    "    mutation=PolynomialMutation(probability=1.0 / problem.number_of_variables, distribution_index=20),\n",
+    "    crossover=SBXCrossover(probability=1.0, distribution_index=20),\n",
+    "    termination_criterion=StoppingByEvaluations(max_evaluations)\n",
+    ")\n",
+    "\n",
+    "algorithm.run()\n",
+    "front = algorithm.get_result()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can now visualize the Pareto front approximation:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "pycharm": {
+     "is_executing": false,
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "
    "
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from jmetal.lab.visualization.plotting import Plot\n",
+    "\n",
+    "plot_front = Plot(plot_title='Pareto front approximation', axis_labels=['x', 'y'])\n",
+    "plot_front.plot(front, label='IBEA-ZDT1')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### API"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "metadata": {
+    "pycharm": {
+     "is_executing": false,
+     "name": "#%% raw\n"
+    },
+    "raw_mimetype": "text/restructuredtext"
+   },
+   "source": [
+    ".. autoclass:: jmetal.algorithm.multiobjective.ibea.IBEA\n",
+    "   :members:\n",
+    "   :undoc-members:\n",
+    "   :show-inheritance:\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "celltoolbar": "Raw Cell Format",
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  },
+  "pycharm": {
+   "stem_cell": {
+    "cell_type": "raw",
+    "metadata": {
+     "collapsed": false
+    },
+    "source": []
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/docs/source/api/algorithm/multiobjective/eas/mocell.ipynb b/docs/source/api/algorithm/multiobjective/eas/mocell.ipynb
new file mode 100644
index 00000000..d99d768d
--- /dev/null
+++ b/docs/source/api/algorithm/multiobjective/eas/mocell.ipynb
@@ -0,0 +1,154 @@
+{
+ "cells": [
+  {
+   "cell_type": "raw",
+   "metadata": {
+    "raw_mimetype": "text/restructuredtext"
+   },
+   "source": [
+    ".. _nb_mocell:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## MOCell"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Example"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "pycharm": {
+     "is_executing": false,
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from jmetal.algorithm.multiobjective.mocell import MOCell\n",
+    "from jmetal.operator import SBXCrossover, PolynomialMutation\n",
+    "from jmetal.problem import ZDT4\n",
+    "from jmetal.util.archive import CrowdingDistanceArchive\n",
+    "from jmetal.util.neighborhood import C9\n",
+    "from jmetal.util.termination_criterion import StoppingByEvaluations\n",
+    "\n",
+    "problem = ZDT4()\n",
+    "\n",
+    "max_evaluations = 25000\n",
+    "\n",
+    "algorithm = MOCell(\n",
+    "    problem=problem,\n",
+    "    population_size=100,\n",
+    "    neighborhood=C9(10, 10),\n",
+    "    archive=CrowdingDistanceArchive(100),\n",
+    "    mutation=PolynomialMutation(probability=1.0 / problem.number_of_variables, distribution_index=20),\n",
+    "    crossover=SBXCrossover(probability=1.0, distribution_index=20),\n",
+    "    termination_criterion=StoppingByEvaluations(max=max_evaluations)\n",
+    ")\n",
+    "\n",
+    "algorithm.run()\n",
+    "front = algorithm.get_result()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can now visualize the Pareto front approximation:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "pycharm": {
+     "is_executing": false,
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "
    "
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from jmetal.lab.visualization.plotting import Plot\n",
+    "\n",
+    "plot_front = Plot(plot_title='Pareto front approximation', axis_labels=['x', 'y'])\n",
+    "plot_front.plot(front, label='MOCell-ZDT4')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### API"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "metadata": {
+    "pycharm": {
+     "is_executing": false,
+     "name": "#%% raw\n"
+    },
+    "raw_mimetype": "text/restructuredtext"
+   },
+   "source": [
+    ".. autoclass:: jmetal.algorithm.multiobjective.mocell.MOCell\n",
+    "   :members:\n",
+    "   :undoc-members:\n",
+    "   :show-inheritance:\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "celltoolbar": "Raw Cell Format",
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  },
+  "pycharm": {
+   "stem_cell": {
+    "cell_type": "raw",
+    "metadata": {
+     "collapsed": false
+    },
+    "source": []
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/docs/source/api/algorithm/multiobjective/eas/moead.ipynb b/docs/source/api/algorithm/multiobjective/eas/moead.ipynb
new file mode 100644
index 00000000..530dfc41
--- /dev/null
+++ b/docs/source/api/algorithm/multiobjective/eas/moead.ipynb
@@ -0,0 +1,159 @@
+{
+ "cells": [
+  {
+   "cell_type": "raw",
+   "metadata": {
+    "raw_mimetype": "text/restructuredtext"
+   },
+   "source": [
+    ".. _nb_moead:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## MOEA/D"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Example"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "pycharm": {
+     "is_executing": false,
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from jmetal.algorithm.multiobjective.moead import MOEAD\n",
+    "from jmetal.operator import PolynomialMutation, DifferentialEvolutionCrossover\n",
+    "from jmetal.problem import LZ09_F2\n",
+    "from jmetal.util.aggregative_function import Tschebycheff\n",
+    "from jmetal.util.termination_criterion import StoppingByEvaluations\n",
+    "\n",
+    "problem = LZ09_F2()\n",
+    "\n",
+    "max_evaluations = 150000\n",
+    "\n",
+    "algorithm = MOEAD(\n",
+    "    problem=problem,\n",
+    "    population_size=300,\n",
+    "    crossover=DifferentialEvolutionCrossover(CR=1.0, F=0.5, K=0.5),\n",
+    "    mutation=PolynomialMutation(probability=1.0 / problem.number_of_variables, distribution_index=20),\n",
+    "    aggregative_function=Tschebycheff(dimension=problem.number_of_objectives),\n",
+    "    neighbor_size=20,\n",
+    "    neighbourhood_selection_probability=0.9,\n",
+    "    max_number_of_replaced_solutions=2,\n",
+    "    weight_files_path='resources/MOEAD_weights',\n",
+    "    termination_criterion=StoppingByEvaluations(max=max_evaluations)\n",
+    ")\n",
+    "\n",
+    "algorithm.run()\n",
+    "solutions = algorithm.get_result()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can now visualize the Pareto front approximation:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "pycharm": {
+     "is_executing": false,
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "
    "
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from jmetal.lab.visualization.plotting import Plot\n",
+    "from jmetal.util.solution import get_non_dominated_solutions\n",
+    "\n",
+    "front = get_non_dominated_solutions(solutions)\n",
+    "    \n",
+    "plot_front = Plot(plot_title='Pareto front approximation', axis_labels=['x', 'y'])\n",
+    "plot_front.plot(front, label='MOEAD-LZ09_F2')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### API"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "metadata": {
+    "pycharm": {
+     "is_executing": false,
+     "name": "#%% raw\n"
+    },
+    "raw_mimetype": "text/restructuredtext"
+   },
+   "source": [
+    ".. autoclass:: jmetal.algorithm.multiobjective.moead.MOEAD\n",
+    "   :members:\n",
+    "   :undoc-members:\n",
+    "   :show-inheritance:\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "celltoolbar": "Raw Cell Format",
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  },
+  "pycharm": {
+   "stem_cell": {
+    "cell_type": "raw",
+    "metadata": {
+     "collapsed": false
+    },
+    "source": []
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/docs/source/api/algorithm/multiobjective/eas/nsgaii.ipynb b/docs/source/api/algorithm/multiobjective/eas/nsgaii.ipynb
new file mode 100644
index 00000000..13bd1208
--- /dev/null
+++ b/docs/source/api/algorithm/multiobjective/eas/nsgaii.ipynb
@@ -0,0 +1,154 @@
+{
+ "cells": [
+  {
+   "cell_type": "raw",
+   "metadata": {
+    "raw_mimetype": "text/restructuredtext"
+   },
+   "source": [
+    ".. _nb_nsgaii:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## NSGA-II"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Example"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "pycharm": {
+     "is_executing": false,
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from jmetal.algorithm.multiobjective.nsgaii import NSGAII\n",
+    "from jmetal.operator import SBXCrossover, PolynomialMutation\n",
+    "from jmetal.problem import ZDT1\n",
+    "from jmetal.util.termination_criterion import StoppingByEvaluations\n",
+    "\n",
+    "problem = ZDT1()\n",
+    "\n",
+    "max_evaluations = 25000\n",
+    "\n",
+    "algorithm = NSGAII(\n",
+    "    problem=problem,\n",
+    "    population_size=100,\n",
+    "    offspring_population_size=100,\n",
+    "    mutation=PolynomialMutation(probability=1.0 / problem.number_of_variables, distribution_index=20),\n",
+    "    crossover=SBXCrossover(probability=1.0, distribution_index=20),\n",
+    "    termination_criterion=StoppingByEvaluations(max=max_evaluations)\n",
+    ")\n",
+    "\n",
+    "algorithm.run()\n",
+    "solutions = algorithm.get_result()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can now visualize the Pareto front approximation:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "pycharm": {
+     "is_executing": true,
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "
    "
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from jmetal.lab.visualization.plotting import Plot\n",
+    "from jmetal.util.solution import get_non_dominated_solutions\n",
+    "\n",
+    "front = get_non_dominated_solutions(solutions)\n",
+    "\n",
+    "plot_front = Plot(plot_title='Pareto front approximation', axis_labels=['x', 'y'])\n",
+    "plot_front.plot(front, label='NSGAII-ZDT1')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### API"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "metadata": {
+    "pycharm": {
+     "is_executing": false,
+     "name": "#%% raw\n"
+    },
+    "raw_mimetype": "text/restructuredtext"
+   },
+   "source": [
+    ".. autoclass:: jmetal.algorithm.multiobjective.nsgaii.NSGAII\n",
+    "   :members:\n",
+    "   :undoc-members:\n",
+    "   :show-inheritance:\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "celltoolbar": "Raw Cell Format",
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  },
+  "pycharm": {
+   "stem_cell": {
+    "cell_type": "raw",
+    "metadata": {
+     "collapsed": false
+    },
+    "source": []
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/docs/source/api/algorithm/multiobjective/eas/nsgaii_distributed.ipynb b/docs/source/api/algorithm/multiobjective/eas/nsgaii_distributed.ipynb
new file mode 100644
index 00000000..4389dafe
--- /dev/null
+++ b/docs/source/api/algorithm/multiobjective/eas/nsgaii_distributed.ipynb
@@ -0,0 +1,218 @@
+{
+ "cells": [
+  {
+   "cell_type": "raw",
+   "metadata": {
+    "raw_mimetype": "text/restructuredtext"
+   },
+   "source": [
+    ".. _nb_dnsgaii:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Distributed NSGA-II"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Example"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "pycharm": {
+     "is_executing": false,
+     "name": "#%%\n"
+    },
+    "tags": [
+     "hide_input"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "from math import sqrt\n",
+    "\n",
+    "from jmetal.core.problem import FloatProblem\n",
+    "from jmetal.core.solution import FloatSolution\n",
+    "\n",
+    "\n",
+    "class ZDT1Modified(FloatProblem):\n",
+    "\n",
+    "    def __init__(self, number_of_variables: int=30):\n",
+    "        \"\"\" :param number_of_variables: Number of decision variables of the problem.\n",
+    "        \"\"\"\n",
+    "        super(ZDT1Modified, self).__init__()\n",
+    "        self.number_of_variables = number_of_variables\n",
+    "        self.number_of_objectives = 2\n",
+    "        self.number_of_constraints = 0\n",
+    "\n",
+    "        self.obj_directions = [self.MINIMIZE, self.MINIMIZE]\n",
+    "        self.obj_labels = ['f(x)', 'f(y)']\n",
+    "\n",
+    "        self.lower_bound = self.number_of_variables * [0.0]\n",
+    "        self.upper_bound = self.number_of_variables * [1.0]\n",
+    "\n",
+    "    def evaluate(self, solution: FloatSolution) -> FloatSolution:\n",
+    "        g = self.__eval_g(solution)\n",
+    "        h = self.__eval_h(solution.variables[0], g)\n",
+    "\n",
+    "        solution.objectives[0] = solution.variables[0]\n",
+    "        solution.objectives[1] = h * g\n",
+    "\n",
+    "        s: float = 0.0\n",
+    "        for i in range(1000):\n",
+    "            for j in range(1000):\n",
+    "                s += i * 0.235 / 1.234 + 1.23525 * j\n",
+    "\n",
+    "        return solution\n",
+    "\n",
+    "    def __eval_g(self, solution: FloatSolution):\n",
+    "        g = sum(solution.variables) - solution.variables[0]\n",
+    "\n",
+    "        constant = 9.0 / (solution.number_of_variables - 1)\n",
+    "        g = constant * g\n",
+    "        g = g + 1.0\n",
+    "\n",
+    "        return g\n",
+    "\n",
+    "    def __eval_h(self, f: float, g: float) -> float:\n",
+    "        return 1.0 - sqrt(f / g)\n",
+    "\n",
+    "    def get_name(self):\n",
+    "        return 'ZDT1'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "pycharm": {
+     "is_executing": false,
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from dask.distributed import Client\n",
+    "from distributed import LocalCluster\n",
+    "\n",
+    "from jmetal.algorithm.multiobjective.nsgaii import DistributedNSGAII\n",
+    "from jmetal.operator import PolynomialMutation, SBXCrossover\n",
+    "from jmetal.util.termination_criterion import StoppingByEvaluations\n",
+    "\n",
+    "# ZDT1 version including a loop for increasing the computing time \n",
+    "#  of the evaluation functions\n",
+    "problem = ZDT1Modified()\n",
+    "\n",
+    "# setup Dask client\n",
+    "client = Client(LocalCluster(n_workers=24))\n",
+    "\n",
+    "ncores = sum(client.ncores().values())\n",
+    "print(f'{ncores} cores available')\n",
+    "\n",
+    "# creates the algorithm\n",
+    "max_evaluations = 25000\n",
+    "\n",
+    "algorithm = DistributedNSGAII(\n",
+    "    problem=problem,\n",
+    "    population_size=100,\n",
+    "    mutation=PolynomialMutation(probability=1.0 / problem.number_of_variables, distribution_index=20),\n",
+    "    crossover=SBXCrossover(probability=1.0, distribution_index=20),\n",
+    "    termination_criterion=StoppingByEvaluations(max=max_evaluations),\n",
+    "    number_of_cores=ncores,\n",
+    "    client=client\n",
+    ")\n",
+    "\n",
+    "algorithm.run()\n",
+    "solutions = algorithm.get_result()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can now visualize the Pareto front approximation:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from jmetal.lab.visualization.plotting import Plot\n",
+    "from jmetal.util.solution import get_non_dominated_solutions\n",
+    "\n",
+    "front = get_non_dominated_solutions(solutions)\n",
+    "    \n",
+    "plot_front = Plot(plot_title='Pareto front approximation', axis_labels=['x', 'y'])\n",
+    "plot_front.plot(front, label='Distributed NSGAII-ZDT1')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### API"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "metadata": {
+    "pycharm": {
+     "is_executing": false,
+     "name": "#%% raw\n"
+    },
+    "raw_mimetype": "text/restructuredtext"
+   },
+   "source": [
+    ".. autoclass:: jmetal.algorithm.multiobjective.nsgaii.DistributedNSGAII\n",
+    "   :members:\n",
+    "   :undoc-members:\n",
+    "   :show-inheritance:\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "celltoolbar": "Tags",
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  },
+  "pycharm": {
+   "stem_cell": {
+    "cell_type": "raw",
+    "source": [],
+    "metadata": {
+     "collapsed": false
+    }
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
\ No newline at end of file
diff --git a/docs/source/api/algorithm/multiobjective/eas/nsgaii_dynamic.ipynb b/docs/source/api/algorithm/multiobjective/eas/nsgaii_dynamic.ipynb
new file mode 100644
index 00000000..6e265c82
--- /dev/null
+++ b/docs/source/api/algorithm/multiobjective/eas/nsgaii_dynamic.ipynb
@@ -0,0 +1,121 @@
+{
+ "cells": [
+  {
+   "cell_type": "raw",
+   "metadata": {
+    "raw_mimetype": "text/restructuredtext"
+   },
+   "source": [
+    ".. _nb_nsgaii_dynamic:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Dynamic NSGA-II"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Example"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "pycharm": {
+     "is_executing": false,
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from jmetal.algorithm.multiobjective.nsgaii import DynamicNSGAII\n",
+    "from jmetal.operator import PolynomialMutation, SBXCrossover\n",
+    "from jmetal.problem.multiobjective.fda import FDA2\n",
+    "from jmetal.util.observable import TimeCounter\n",
+    "from jmetal.util.observer import PlotFrontToFileObserver, WriteFrontToFileObserver\n",
+    "from jmetal.util.termination_criterion import StoppingByEvaluations\n",
+    "\n",
+    "problem = FDA2()\n",
+    "\n",
+    "time_counter = TimeCounter(delay=1)\n",
+    "time_counter.observable.register(problem)\n",
+    "time_counter.start()\n",
+    "\n",
+    "max_evaluations = 25000\n",
+    "algorithm = DynamicNSGAII(\n",
+    "    problem=problem,\n",
+    "    population_size=100,\n",
+    "    offspring_population_size=100,\n",
+    "    mutation=PolynomialMutation(probability=1.0 / problem.number_of_variables, distribution_index=20),\n",
+    "    crossover=SBXCrossover(probability=1.0, distribution_index=20),\n",
+    "    termination_criterion=StoppingByEvaluations(max=max_evaluations)\n",
+    ")\n",
+    "\n",
+    "algorithm.observable.register(observer=PlotFrontToFileObserver('front_plot'))\n",
+    "algorithm.observable.register(observer=WriteFrontToFileObserver('front_files'))\n",
+    "\n",
+    "algorithm.run()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### API"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "metadata": {
+    "pycharm": {
+     "is_executing": false,
+     "name": "#%% raw\n"
+    },
+    "raw_mimetype": "text/restructuredtext"
+   },
+   "source": [
+    ".. autoclass:: jmetal.algorithm.multiobjective.nsgaii.DynamicNSGAII\n",
+    "   :members:\n",
+    "   :undoc-members:\n",
+    "   :show-inheritance:\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "celltoolbar": "Raw Cell Format",
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  },
+  "pycharm": {
+   "stem_cell": {
+    "cell_type": "raw",
+    "source": [],
+    "metadata": {
+     "collapsed": false
+    }
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
\ No newline at end of file
diff --git a/docs/source/api/algorithm/multiobjective/eas/nsgaii_preference.ipynb b/docs/source/api/algorithm/multiobjective/eas/nsgaii_preference.ipynb
new file mode 100644
index 00000000..09d41b55
--- /dev/null
+++ b/docs/source/api/algorithm/multiobjective/eas/nsgaii_preference.ipynb
@@ -0,0 +1,156 @@
+{
+ "cells": [
+  {
+   "cell_type": "raw",
+   "metadata": {
+    "raw_mimetype": "text/restructuredtext"
+   },
+   "source": [
+    ".. _nb_nsgaii_preference:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Preference point-based NSGA-II"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Example"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "pycharm": {
+     "is_executing": false,
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from jmetal.algorithm.multiobjective.nsgaii import NSGAII\n",
+    "from jmetal.operator import SBXCrossover, PolynomialMutation\n",
+    "from jmetal.problem import ZDT2\n",
+    "from jmetal.util.comparator import GDominanceComparator\n",
+    "from jmetal.util.termination_criterion import StoppingByEvaluations\n",
+    "\n",
+    "problem = ZDT2()\n",
+    "\n",
+    "max_evaluations = 25000\n",
+    "reference_point = [0.2, 0.5]\n",
+    "\n",
+    "algorithm = NSGAII(\n",
+    "    problem=problem,\n",
+    "    population_size=100,\n",
+    "    offspring_population_size=100,\n",
+    "    mutation=PolynomialMutation(probability=1.0 / problem.number_of_variables, distribution_index=20),\n",
+    "    crossover=SBXCrossover(probability=1.0, distribution_index=20),\n",
+    "    dominance_comparator=GDominanceComparator(reference_point),\n",
+    "    termination_criterion=StoppingByEvaluations(max=max_evaluations)\n",
+    ")\n",
+    "\n",
+    "algorithm.run()\n",
+    "solutions = algorithm.get_result()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can now visualize the Pareto front approximation:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "
    "
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from jmetal.lab.visualization.plotting import Plot\n",
+    "from jmetal.util.solution import get_non_dominated_solutions\n",
+    "\n",
+    "front = get_non_dominated_solutions(solutions)\n",
+    "    \n",
+    "plot_front = Plot(plot_title='Pareto front approximation', axis_labels=['x', 'y'], reference_point=reference_point)\n",
+    "plot_front.plot(front, label='gNSGAII-ZDT1')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### API"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "metadata": {
+    "pycharm": {
+     "is_executing": false,
+     "name": "#%% raw\n"
+    },
+    "raw_mimetype": "text/restructuredtext"
+   },
+   "source": [
+    ".. autoclass:: jmetal.algorithm.multiobjective.nsgaii.DynamicNSGAII\n",
+    "   :members:\n",
+    "   :undoc-members:\n",
+    "   :show-inheritance:\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "celltoolbar": "Raw Cell Format",
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  },
+  "pycharm": {
+   "stem_cell": {
+    "cell_type": "raw",
+    "source": [],
+    "metadata": {
+     "collapsed": false
+    }
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
\ No newline at end of file
diff --git a/docs/source/api/algorithm/multiobjective/eas/nsgaiii.ipynb b/docs/source/api/algorithm/multiobjective/eas/nsgaiii.ipynb
new file mode 100644
index 00000000..f409a53a
--- /dev/null
+++ b/docs/source/api/algorithm/multiobjective/eas/nsgaiii.ipynb
@@ -0,0 +1,74 @@
+{
+ "cells": [
+  {
+   "cell_type": "raw",
+   "metadata": {
+    "raw_mimetype": "text/restructuredtext"
+   },
+   "source": [
+    ".. _nb_nsgaiii:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## NSGA-III"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### API"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "metadata": {
+    "pycharm": {
+     "is_executing": false,
+     "name": "#%% raw\n"
+    },
+    "raw_mimetype": "text/restructuredtext"
+   },
+   "source": [
+    ".. autoclass:: jmetal.algorithm.multiobjective.nsgaiii.NSGAIII\n",
+    "   :members:\n",
+    "   :undoc-members:\n",
+    "   :show-inheritance:\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "celltoolbar": "Raw Cell Format",
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  },
+  "pycharm": {
+   "stem_cell": {
+    "cell_type": "raw",
+    "source": [],
+    "metadata": {
+     "collapsed": false
+    }
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
\ No newline at end of file
diff --git a/docs/source/api/algorithm/multiobjective/eas/spea2.ipynb b/docs/source/api/algorithm/multiobjective/eas/spea2.ipynb
new file mode 100644
index 00000000..d75c46c2
--- /dev/null
+++ b/docs/source/api/algorithm/multiobjective/eas/spea2.ipynb
@@ -0,0 +1,154 @@
+{
+ "cells": [
+  {
+   "cell_type": "raw",
+   "metadata": {
+    "raw_mimetype": "text/restructuredtext"
+   },
+   "source": [
+    ".. _nb_spea2:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## SPEA2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Example"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "pycharm": {
+     "is_executing": false,
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from jmetal.algorithm.multiobjective.spea2 import SPEA2\n",
+    "from jmetal.operator import SBXCrossover, PolynomialMutation\n",
+    "from jmetal.problem import ZDT1\n",
+    "from jmetal.util.termination_criterion import StoppingByEvaluations\n",
+    "\n",
+    "problem = ZDT1()\n",
+    "\n",
+    "max_evaluations = 20000\n",
+    "\n",
+    "algorithm = SPEA2(\n",
+    "    problem=problem,\n",
+    "    population_size=40,\n",
+    "    offspring_population_size=40,\n",
+    "    mutation=PolynomialMutation(probability=1.0 / problem.number_of_variables, distribution_index=20),\n",
+    "    crossover=SBXCrossover(probability=1.0, distribution_index=20),\n",
+    "    termination_criterion=StoppingByEvaluations(max=max_evaluations)\n",
+    ")\n",
+    "\n",
+    "algorithm.run()\n",
+    "solutions = algorithm.get_result()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can now visualize the Pareto front approximation:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "pycharm": {
+     "is_executing": false,
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "
    "
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from jmetal.lab.visualization.plotting import Plot\n",
+    "from jmetal.util.solution import get_non_dominated_solutions\n",
+    "\n",
+    "front = get_non_dominated_solutions(solutions)\n",
+    "    \n",
+    "plot_front = Plot(plot_title='Pareto front approximation', axis_labels=['x', 'y'])\n",
+    "plot_front.plot(front, label='SPEA2-ZDT1')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### API"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "metadata": {
+    "pycharm": {
+     "is_executing": false,
+     "name": "#%% raw\n"
+    },
+    "raw_mimetype": "text/restructuredtext"
+   },
+   "source": [
+    ".. autoclass:: jmetal.algorithm.multiobjective.spea2.SPEA2\n",
+    "   :members:\n",
+    "   :undoc-members:\n",
+    "   :show-inheritance:\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "celltoolbar": "Raw Cell Format",
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  },
+  "pycharm": {
+   "stem_cell": {
+    "cell_type": "raw",
+    "metadata": {
+     "collapsed": false
+    },
+    "source": []
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/docs/source/api/algorithm/multiobjective/pso.rst b/docs/source/api/algorithm/multiobjective/pso.rst
new file mode 100644
index 00000000..1aadd9f7
--- /dev/null
+++ b/docs/source/api/algorithm/multiobjective/pso.rst
@@ -0,0 +1,9 @@
+PSO Algorithms
+=======================
+
+.. toctree::
+   :maxdepth: 1
+   :caption: Multi-objective particle swarm optimization algorithms:
+
+   psos/omopso
+   psos/smpso
\ No newline at end of file
diff --git a/docs/source/api/algorithm/multiobjective/psos/omopso.ipynb b/docs/source/api/algorithm/multiobjective/psos/omopso.ipynb
new file mode 100644
index 00000000..15d94311
--- /dev/null
+++ b/docs/source/api/algorithm/multiobjective/psos/omopso.ipynb
@@ -0,0 +1,160 @@
+{
+ "cells": [
+  {
+   "cell_type": "raw",
+   "metadata": {
+    "raw_mimetype": "text/restructuredtext"
+   },
+   "source": [
+    ".. _nb_omopso:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## OMOPSO"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Example"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "pycharm": {
+     "is_executing": false,
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from jmetal.algorithm.multiobjective.omopso import OMOPSO\n",
+    "from jmetal.operator import UniformMutation\n",
+    "from jmetal.operator.mutation import NonUniformMutation\n",
+    "from jmetal.problem import ZDT1\n",
+    "from jmetal.util.archive import CrowdingDistanceArchive\n",
+    "from jmetal.util.termination_criterion import StoppingByEvaluations\n",
+    "\n",
+    "problem = ZDT1()\n",
+    "\n",
+    "mutation_probability = 1.0 / problem.number_of_variables\n",
+    "max_evaluations = 25000\n",
+    "swarm_size = 100\n",
+    "\n",
+    "algorithm = OMOPSO(\n",
+    "    problem=problem,\n",
+    "    swarm_size=swarm_size,\n",
+    "    epsilon=0.0075,\n",
+    "    uniform_mutation=UniformMutation(probability=mutation_probability, perturbation=0.5),\n",
+    "    non_uniform_mutation=NonUniformMutation(mutation_probability, perturbation=0.5,\n",
+    "                                            max_iterations=int(max_evaluations / swarm_size)),\n",
+    "    leaders=CrowdingDistanceArchive(100),\n",
+    "    termination_criterion=StoppingByEvaluations(max=max_evaluations)\n",
+    ")\n",
+    "\n",
+    "algorithm.run()\n",
+    "solutions = algorithm.get_result()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can now visualize the Pareto front approximation:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "pycharm": {
+     "is_executing": true,
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "
    "
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from jmetal.lab.visualization.plotting import Plot\n",
+    "from jmetal.util.solution import get_non_dominated_solutions\n",
+    "\n",
+    "front = get_non_dominated_solutions(solutions)\n",
+    "\n",
+    "plot_front = Plot(plot_title='Pareto front approximation', axis_labels=['x', 'y'])\n",
+    "plot_front.plot(front, label='OMOPSO-ZDT1')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### API"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "metadata": {
+    "pycharm": {
+     "is_executing": false,
+     "name": "#%% raw\n"
+    },
+    "raw_mimetype": "text/restructuredtext"
+   },
+   "source": [
+    ".. autoclass:: jmetal.algorithm.multiobjective.omopso.OMOPSO\n",
+    "   :members:\n",
+    "   :undoc-members:\n",
+    "   :show-inheritance:\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "celltoolbar": "Raw Cell Format",
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  },
+  "pycharm": {
+   "stem_cell": {
+    "cell_type": "raw",
+    "metadata": {
+     "collapsed": false
+    },
+    "source": []
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/docs/source/api/algorithm/multiobjective/psos/smpso.ipynb b/docs/source/api/algorithm/multiobjective/psos/smpso.ipynb
new file mode 100644
index 00000000..7540ecb3
--- /dev/null
+++ b/docs/source/api/algorithm/multiobjective/psos/smpso.ipynb
@@ -0,0 +1,153 @@
+{
+ "cells": [
+  {
+   "cell_type": "raw",
+   "metadata": {
+    "raw_mimetype": "text/restructuredtext"
+   },
+   "source": [
+    ".. _nb_smpso:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## SMPSO"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Example"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "pycharm": {
+     "is_executing": true,
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from jmetal.algorithm.multiobjective.smpso import SMPSO\n",
+    "from jmetal.operator import PolynomialMutation\n",
+    "from jmetal.problem import ZDT4\n",
+    "from jmetal.util.archive import CrowdingDistanceArchive\n",
+    "from jmetal.util.termination_criterion import StoppingByEvaluations\n",
+    "\n",
+    "problem = ZDT4()\n",
+    "\n",
+    "max_evaluations = 25000\n",
+    "algorithm = SMPSO(\n",
+    "    problem=problem,\n",
+    "    swarm_size=100,\n",
+    "    mutation=PolynomialMutation(probability=1.0 / problem.number_of_variables, distribution_index=20),\n",
+    "    leaders=CrowdingDistanceArchive(100),\n",
+    "    termination_criterion=StoppingByEvaluations(max=max_evaluations)\n",
+    ")\n",
+    "\n",
+    "algorithm.run()\n",
+    "solutions = algorithm.get_result()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can now visualize the Pareto front approximation:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "pycharm": {
+     "is_executing": true,
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "
    "
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from jmetal.lab.visualization.plotting import Plot\n",
+    "from jmetal.util.solution import get_non_dominated_solutions\n",
+    "\n",
+    "front = get_non_dominated_solutions(solutions)\n",
+    "\n",
+    "plot_front = Plot(plot_title='Pareto front approximation', axis_labels=['x', 'y'])\n",
+    "plot_front.plot(front, label='SMPSO-ZDT4')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### API"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "metadata": {
+    "pycharm": {
+     "is_executing": false,
+     "name": "#%% raw\n"
+    },
+    "raw_mimetype": "text/restructuredtext"
+   },
+   "source": [
+    ".. autoclass:: jmetal.algorithm.multiobjective.smpso.SMPSO\n",
+    "   :members:\n",
+    "   :undoc-members:\n",
+    "   :show-inheritance:\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "celltoolbar": "Raw Cell Format",
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  },
+  "pycharm": {
+   "stem_cell": {
+    "cell_type": "raw",
+    "metadata": {
+     "collapsed": false
+    },
+    "source": []
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/docs/source/api/algorithm/multiobjective/psos/smpso_dynamic.ipynb b/docs/source/api/algorithm/multiobjective/psos/smpso_dynamic.ipynb
new file mode 100644
index 00000000..7899394a
--- /dev/null
+++ b/docs/source/api/algorithm/multiobjective/psos/smpso_dynamic.ipynb
@@ -0,0 +1,121 @@
+{
+ "cells": [
+  {
+   "cell_type": "raw",
+   "metadata": {
+    "raw_mimetype": "text/restructuredtext"
+   },
+   "source": [
+    ".. _nb_smpso_dynamic:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Dynamic SMPSO"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Example"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "pycharm": {
+     "is_executing": false,
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from jmetal.algorithm.multiobjective.smpso import DynamicSMPSO\n",
+    "from jmetal.operator import PolynomialMutation\n",
+    "from jmetal.problem.multiobjective.fda import FDA2\n",
+    "from jmetal.util.archive import CrowdingDistanceArchive\n",
+    "from jmetal.util.observable import TimeCounter\n",
+    "from jmetal.util.observer import PlotFrontToFileObserver, WriteFrontToFileObserver\n",
+    "from jmetal.util.termination_criterion import StoppingByEvaluations\n",
+    "\n",
+    "problem = FDA2()\n",
+    "\n",
+    "time_counter = TimeCounter(delay=15)\n",
+    "time_counter.observable.register(problem)\n",
+    "time_counter.start()\n",
+    "\n",
+    "max_evaluations = 25000\n",
+    "algorithm = DynamicSMPSO(\n",
+    "    problem=problem,\n",
+    "    swarm_size=100,\n",
+    "    mutation=PolynomialMutation(probability=1.0 / problem.number_of_variables, distribution_index=20),\n",
+    "    leaders=CrowdingDistanceArchive(100),\n",
+    "    termination_criterion=StoppingByEvaluations(max=max_evaluations)\n",
+    ")\n",
+    "\n",
+    "algorithm.observable.register(observer=PlotFrontToFileObserver('front_plot'))\n",
+    "algorithm.observable.register(observer=WriteFrontToFileObserver('front_files'))\n",
+    "\n",
+    "algorithm.run()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### API"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "metadata": {
+    "pycharm": {
+     "is_executing": false,
+     "name": "#%% raw\n"
+    },
+    "raw_mimetype": "text/restructuredtext"
+   },
+   "source": [
+    ".. autoclass:: jmetal.algorithm.multiobjective.smpso.DynamicSMPSO\n",
+    "   :members:\n",
+    "   :undoc-members:\n",
+    "   :show-inheritance:\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "celltoolbar": "Raw Cell Format",
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  },
+  "pycharm": {
+   "stem_cell": {
+    "cell_type": "raw",
+    "metadata": {
+     "collapsed": false
+    },
+    "source": []
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/docs/source/api/algorithm/multiobjective/psos/smpso_preference.ipynb b/docs/source/api/algorithm/multiobjective/psos/smpso_preference.ipynb
new file mode 100644
index 00000000..571a311a
--- /dev/null
+++ b/docs/source/api/algorithm/multiobjective/psos/smpso_preference.ipynb
@@ -0,0 +1,172 @@
+{
+ "cells": [
+  {
+   "cell_type": "raw",
+   "metadata": {
+    "raw_mimetype": "text/restructuredtext"
+   },
+   "source": [
+    ".. _nb_smpso_preference:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## SMPSO/RP"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Example"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "pycharm": {
+     "is_executing": false,
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Enter 2-points of dimension 2: \n"
+     ]
+    }
+   ],
+   "source": [
+    "from jmetal.algorithm.multiobjective.smpso import SMPSORP\n",
+    "from jmetal.operator import PolynomialMutation\n",
+    "from jmetal.problem import ZDT4\n",
+    "from jmetal.util.archive import CrowdingDistanceArchiveWithReferencePoint\n",
+    "from jmetal.util.termination_criterion import StoppingByEvaluations\n",
+    "\n",
+    "problem = ZDT4()\n",
+    "\n",
+    "swarm_size = 100\n",
+    "\n",
+    "reference_point = [[0.1, 0.8],[0.6, 0.1]]\n",
+    "archives_with_reference_points = []\n",
+    "\n",
+    "for point in reference_point:\n",
+    "    archives_with_reference_points.append(\n",
+    "        CrowdingDistanceArchiveWithReferencePoint(int(swarm_size / len(reference_point)), point)\n",
+    "    )\n",
+    "\n",
+    "max_evaluations = 50000\n",
+    "algorithm = SMPSORP(\n",
+    "    problem=problem,\n",
+    "    swarm_size=swarm_size,\n",
+    "    mutation=PolynomialMutation(probability=1.0 / problem.number_of_variables, distribution_index=20),\n",
+    "    reference_points=reference_point,\n",
+    "    leaders=archives_with_reference_points,\n",
+    "    termination_criterion=StoppingByEvaluations(max=max_evaluations)\n",
+    ")\n",
+    "\n",
+    "algorithm.run()\n",
+    "solutions = algorithm.get_result()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can now visualize the Pareto front approximation:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "pycharm": {
+     "is_executing": true,
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "
    "
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from jmetal.lab.visualization.plotting import Plot\n",
+    "from jmetal.util.solution import get_non_dominated_solutions\n",
+    "\n",
+    "front = get_non_dominated_solutions(solutions)\n",
+    "\n",
+    "plot_front = Plot(plot_title='Pareto front approximation', axis_labels=['x', 'y'], reference_point=reference_point)\n",
+    "plot_front.plot(front, label='SMPSORP-ZDT4')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### API"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "metadata": {
+    "pycharm": {
+     "is_executing": false,
+     "name": "#%% raw\n"
+    },
+    "raw_mimetype": "text/restructuredtext"
+   },
+   "source": [
+    ".. autoclass:: jmetal.algorithm.multiobjective.smpso.SMPSORP\n",
+    "   :members:\n",
+    "   :undoc-members:\n",
+    "   :show-inheritance:\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "celltoolbar": "Raw Cell Format",
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  },
+  "pycharm": {
+   "stem_cell": {
+    "cell_type": "raw",
+    "metadata": {
+     "collapsed": false
+    },
+    "source": []
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/docs/source/api/algorithm/singleobjective/evolution.strategy.rst b/docs/source/api/algorithm/singleobjective/evolution.strategy.rst
new file mode 100644
index 00000000..42ba79c5
--- /dev/null
+++ b/docs/source/api/algorithm/singleobjective/evolution.strategy.rst
@@ -0,0 +1,8 @@
+Evolution Strategy
+-----------------------------------------------------------
+
+.. automodule:: jmetal.algorithm.singleobjective.evolution_strategy
+   :members:
+   :undoc-members:
+   :show-inheritance:
+   :exclude-members: R
diff --git a/docs/source/api/algorithm/singleobjective/genetic.algorithm.rst b/docs/source/api/algorithm/singleobjective/genetic.algorithm.rst
new file mode 100644
index 00000000..02311f38
--- /dev/null
+++ b/docs/source/api/algorithm/singleobjective/genetic.algorithm.rst
@@ -0,0 +1,8 @@
+Genetic Algorithm
+----------------------------------------------------------
+
+.. automodule:: jmetal.algorithm.singleobjective.genetic_algorithm
+   :members:
+   :undoc-members:
+   :show-inheritance:
+   :exclude-members: R
diff --git a/docs/source/api/algorithm/singleobjective/local.search.rst b/docs/source/api/algorithm/singleobjective/local.search.rst
new file mode 100644
index 00000000..ba5971d6
--- /dev/null
+++ b/docs/source/api/algorithm/singleobjective/local.search.rst
@@ -0,0 +1,8 @@
+Local Search
+-----------------------------------------------------
+
+.. automodule:: jmetal.algorithm.singleobjective.local_search
+   :members:
+   :undoc-members:
+   :show-inheritance:
+   :exclude-members: R
diff --git a/docs/source/api/algorithm/singleobjective/simulated.annealing.rst b/docs/source/api/algorithm/singleobjective/simulated.annealing.rst
new file mode 100644
index 00000000..5a3e53af
--- /dev/null
+++ b/docs/source/api/algorithm/singleobjective/simulated.annealing.rst
@@ -0,0 +1,8 @@
+Simulated annealing
+------------------------------------------------------------
+
+.. automodule:: jmetal.algorithm.singleobjective.simulated_annealing
+   :members:
+   :undoc-members:
+   :show-inheritance:
+   :exclude-members: R
diff --git a/docs/source/api/jmetal.lab.statistical_test.rst b/docs/source/api/jmetal.lab.statistical_test.rst
new file mode 100644
index 00000000..718a780b
--- /dev/null
+++ b/docs/source/api/jmetal.lab.statistical_test.rst
@@ -0,0 +1,46 @@
+jmetal.lab.statistical\_test package
+====================================
+
+Submodules
+----------
+
+jmetal.lab.statistical\_test.apv\_procedures module
+---------------------------------------------------
+
+.. automodule:: jmetal.lab.statistical_test.apv_procedures
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+jmetal.lab.statistical\_test.bayesian module
+--------------------------------------------
+
+.. automodule:: jmetal.lab.statistical_test.bayesian
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+jmetal.lab.statistical\_test.critical\_distance module
+------------------------------------------------------
+
+.. automodule:: jmetal.lab.statistical_test.critical_distance
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+jmetal.lab.statistical\_test.functions module
+---------------------------------------------
+
+.. automodule:: jmetal.lab.statistical_test.functions
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+
+Module contents
+---------------
+
+.. automodule:: jmetal.lab.statistical_test
+   :members:
+   :undoc-members:
+   :show-inheritance:
diff --git a/docs/source/api/operator/crossover.rst b/docs/source/api/operator/crossover.rst
new file mode 100644
index 00000000..eaaf21ea
--- /dev/null
+++ b/docs/source/api/operator/crossover.rst
@@ -0,0 +1,7 @@
+Crossover
+=======================
+
+.. automodule:: jmetal.operator.crossover
+   :members:
+   :undoc-members:
+   :show-inheritance:
diff --git a/docs/source/api/operator/mutation.rst b/docs/source/api/operator/mutation.rst
new file mode 100644
index 00000000..cea3fad0
--- /dev/null
+++ b/docs/source/api/operator/mutation.rst
@@ -0,0 +1,7 @@
+Mutation
+=======================
+
+.. automodule:: jmetal.operator.mutation
+   :members:
+   :undoc-members:
+   :show-inheritance:
\ No newline at end of file
diff --git a/docs/source/api/operator/selection.rst b/docs/source/api/operator/selection.rst
new file mode 100644
index 00000000..e485db22
--- /dev/null
+++ b/docs/source/api/operator/selection.rst
@@ -0,0 +1,7 @@
+Selection
+=======================
+
+.. automodule:: jmetal.operator.selection
+   :members:
+   :undoc-members:
+   :show-inheritance:
diff --git a/docs/source/api/problem/multiobjective.rst b/docs/source/api/problem/multiobjective.rst
new file mode 100644
index 00000000..c3fa1161
--- /dev/null
+++ b/docs/source/api/problem/multiobjective.rst
@@ -0,0 +1,58 @@
+Multi-objective problems
+=====================================
+
+Constrained
+------------------------------------------------
+
+.. automodule:: jmetal.problem.multiobjective.constrained
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+Unconstrained
+--------------------------------------------------
+
+.. automodule:: jmetal.problem.multiobjective.unconstrained
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+ZDT
+----------------------------------------
+
+.. automodule:: jmetal.problem.multiobjective.zdt
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+DTLZ
+-----------------------------------------
+
+.. automodule:: jmetal.problem.multiobjective.dtlz
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+FDA
+----------------------------------------
+
+.. automodule:: jmetal.problem.multiobjective.fda
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+LIRCMOP
+--------------------------------------------
+
+.. automodule:: jmetal.problem.multiobjective.lircmop
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+LZ09
+-----------------------------------------
+
+.. automodule:: jmetal.problem.multiobjective.lz09
+   :members:
+   :undoc-members:
+   :show-inheritance:
diff --git a/docs/source/api/problem/singleobjective.rst b/docs/source/api/problem/singleobjective.rst
new file mode 100644
index 00000000..5e0e36e8
--- /dev/null
+++ b/docs/source/api/problem/singleobjective.rst
@@ -0,0 +1,26 @@
+Single-objective problems
+======================================
+
+Unconstrained
+---------------------------------------------------
+
+.. automodule:: jmetal.problem.singleobjective.unconstrained
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+Knapsack
+----------------------------------------------
+
+.. automodule:: jmetal.problem.singleobjective.knapsack
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+TSP
+-----------------------------------------
+
+.. automodule:: jmetal.problem.singleobjective.tsp
+   :members:
+   :undoc-members:
+   :show-inheritance:
diff --git a/docs/source/index.rst b/docs/source/index.rst
index 2281df58..a2aea3e4 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -30,16 +30,16 @@ Via pip:
 
 .. code-block:: console
 
-    $ pip install jmetalpy
+    $ pip install jmetalpy  # or "jmetalpy[distributed]"
 
 .. note:: Alternatively, you can use one of these instead:
 
     .. code-block:: console
 
-        $ pip install "jmetalpy[core]"  # Install core components of the framework
+        $ pip install "jmetalpy[core]"  # Install core components of the framework (equivalent to `pip install jmetalpy`)
         $ pip install "jmetalpy[docs]"  # Install requirements for building docs
         $ pip install "jmetalpy[distributed]"  # Install requirements for parallel/distributed computing
-        $ pip install "jmetalpy[complete]"  # Install all dependencies (equivalent to `pip install jmetalpy`)
+        $ pip install "jmetalpy[complete]"  # Install all dependencies
 
 Via source code:
 
diff --git a/docs/tutorials.html b/docs/tutorials.html
index 7ad13538..ec19fbee 100644
--- a/docs/tutorials.html
+++ b/docs/tutorials.html
@@ -24,7 +24,6 @@
     
     
     
-    
     
     
     
@@ -89,8 +88,6 @@ 
    Table Of Contents
    
 
  • Single-objective algorithms
    • 
 
  • Operators
    • 
 
  • Problems
    • 
-
  • Contributing
    • 
-
  • About
    • 
 
 
     
diff --git a/docs/tutorials/evaluator.html b/docs/tutorials/evaluator.html
index b1565f5d..6d83df45 100644
--- a/docs/tutorials/evaluator.html
+++ b/docs/tutorials/evaluator.html
@@ -24,7 +24,6 @@
     
     
     
-    
     
     
     
@@ -90,8 +89,6 @@ 
    Table Of Contents
    
 
  • Single-objective algorithms
    • 
 
  • Operators
    • 
 
  • Problems
    • 
-
  • Contributing
    • 
-
  • About
    • 
 
 
     
diff --git a/docs/tutorials/experiment.html b/docs/tutorials/experiment.html
index 11c3a913..b61e0dd4 100644
--- a/docs/tutorials/experiment.html
+++ b/docs/tutorials/experiment.html
@@ -24,7 +24,6 @@
     
     
     
-    
     
     
     
@@ -90,8 +89,6 @@ 
    Table Of Contents
    
 
  • Single-objective algorithms
    • 
 
  • Operators
    • 
 
  • Problems
    • 
-
  • Contributing
    • 
-
  • About
    • 
 
 
     
@@ -105,7 +102,7 @@ 
    Contents
    
 
  • Experiments
 
    • 
 
@@ -251,8 +248,68 @@ 
    Summary file
-
    API
    
+
    
+
    API
    
+
    
+
    
+class jmetal.lab.experiment.Experiment(output_dir: str, jobs: List[jmetal.lab.experiment.Job], m_workers: int = 6)[source]
    
+
    Bases: object
    
+
    
+
    
+run() → None[source]
    
+
    
+
+
    
+
+
    
+
    
+class jmetal.lab.experiment.Job(algorithm: jmetal.core.algorithm.Algorithm, algorithm_tag: str, problem_tag: str, run: int)[source]
    
+
    Bases: object
    
+
    
+
    
+execute(output_path: str = '')[source]
    
+
    
+
+
    
+
+
    
+
    
+jmetal.lab.experiment.generate_summary_from_experiment(input_dir: str, quality_indicators: List[jmetal.core.quality_indicator.QualityIndicator], reference_fronts: str = '')[source]
    
+
    Compute a list of quality indicators. The input data directory must met the following structure (this is generated
+automatically by the Experiment class):
    
+
      
+
    • <base_dir>
      
+
        
+
      • algorithm_a
        
+
          
+
        • problem_a
          
+
            
+
          • FUN.0.tsv
            • 
+
          • FUN.1.tsv
            • 
+
          • VAR.0.tsv
            • 
+
          • VAR.1.tsv
            • 
+
          • …
            • 
+
          
+
          • 
+
        
+
        • 
+
      
+
      • 
+
    
+
    
+
    Parameters
    
+
      
+
    • input_dir – Directory where all the input data is found (function values and variables).
      • 
+
    • reference_fronts – Directory where reference fronts are found.
      • 
+
    • quality_indicators – List of quality indicators to compute.
      • 
+
    
+
    
+
    Returns
    
+
    None.
    
+
    
+
    
+
    
+
 
    
 
    
 
diff --git a/docs/tutorials/observer.html b/docs/tutorials/observer.html
index 2ae0d296..6f7618e7 100644
--- a/docs/tutorials/observer.html
+++ b/docs/tutorials/observer.html
@@ -24,7 +24,6 @@
     
     
     
-    
     
     
     
@@ -90,8 +89,6 @@ 
    Table Of Contents
    
 
  • Single-objective algorithms
    • 
 
  • Operators
    • 
 
  • Problems
    • 
-
  • Contributing
    • 
-
  • About
    • 
 
 
     
diff --git a/docs/tutorials/problem.html b/docs/tutorials/problem.html
index 144d1956..56ed199f 100644
--- a/docs/tutorials/problem.html
+++ b/docs/tutorials/problem.html
@@ -24,7 +24,6 @@
     
     
     
-    
     
     
     
@@ -90,8 +89,6 @@ 
    Table Of Contents
    
 
  • Single-objective algorithms
    • 
 
  • Operators
    • 
 
  • Problems
    • 
-
  • Contributing
    • 
-
  • About
    • 
 
 
     
@@ -155,7 +152,7 @@ 
    Use case: Subset Sum
    class SubsetSum(BinaryProblem):
 
    def __init__(self, C: int, W: list):
-      super(SubsetSum, self).__init__(reference_front=None)
+      super(SubsetSum, self).__init__(reference_front=None)
       self.C = C
       self.W = W
 
@@ -207,7 +204,7 @@ 
    Use case: Subset Sumnew_solution = BinarySolution(number_of_variables=self.number_of_variables,
                                   number_of_objectives=self.number_of_objectives)
     new_solution.variables[0] = \
-        [True if random.randint(0, 1) == 0 else False for _ in range(self.number_of_bits)]
+        [True if random.randint(0, 1) == 0 else False for _ in range(self.number_of_bits)]
 
     return new_solution
 
    
@@ -325,7 +322,7 @@ 
    Use case: Multi-objective Subset Sumjmetal.core.problem.FloatProblem
    
 
    Class for defining float problems on the fly.
    
 
    Example:
    
-
    >>> # Defining problem Srinivas on the fly
+
    >>> # Defining problem Srinivas on the fly
 >>> def f1(x: [float]):
 >>>     return 2.0 + (x[0] - 2.0) * (x[0] - 2.0) + (x[1] - 1.0) * (x[1] - 1.0)
 >>>
diff --git a/docs/tutorials/statistics.html b/docs/tutorials/statistics.html
index ad6e5dc6..314f211f 100644
--- a/docs/tutorials/statistics.html
+++ b/docs/tutorials/statistics.html
@@ -24,7 +24,6 @@
     
     
     
-    
     
     
     
@@ -90,8 +89,6 @@ 
    Table Of Contents
    
 
  • Single-objective algorithms
    • 
 
  • Operators
    • 
 
  • Problems
    • 
-
  • Contributing
    • 
-
  • About
    • 
 
 
     
diff --git a/docs/tutorials/visualization.html b/docs/tutorials/visualization.html
index 466bb71c..7a0dc401 100644
--- a/docs/tutorials/visualization.html
+++ b/docs/tutorials/visualization.html
@@ -24,7 +24,6 @@
     
     
     
-    
     
     
     
@@ -90,8 +89,6 @@ 
    Table Of Contents
    
 
  • Single-objective algorithms
    • 
 
  • Operators
    • 
 
  • Problems
    • 
-
  • Contributing
    • 
-
  • About
    • 
 
 
     
@@ -181,7 +178,7 @@ 
    Static plots
    plot_front = Plot(title='Pareto front approximation', axis_labels=['x', 'y'])
-plot_front.plot([front1, front2], label=['zdt1', 'zdt2'], filename='output', format='eps')
+plot_front.plot([front1, front2], label=['zdt1', 'zdt2'], filename='output', format='eps')