Merge branch 'develop'

.gitignore

@@ -5,9 +5,6 @@ experimental
 *.egg-info
 /venv
 */venv
-cythonize_files.py
 /pymoode/cython/*.html
 /pymoode/cython/*.pyd
 __pycache__/
-cheat_codes.txt
-notebooks/validate_runtime.ipynb

pymoode/algorithms/base/differential.py

@@ -1,3 +1,6 @@
+# Native
+from typing import Optional
+
 # External
 import numpy as np
 
@@ -35,7 +38,7 @@ def __init__(self,
                  **kwargs):
         """
         Base class for Differential Evolution algorithms
-        
+
         Parameters
         ----------
         pop_size : int, optional
@@ -81,7 +84,7 @@ def __init__(self,
 
         genetic_mutation, optional
             Pymoo's genetic mutation operator after crossover. Defaults to NoMutation().
-        
+
         survival : Survival, optional
             Replacement survival operator. Defaults to ImprovementReplacement().
 
@@ -109,7 +112,7 @@ def __init__(self,
             advance_after_initial_infill=advance_after_initial_infill,
             **kwargs,
         )
-        
+
         self.termination = DefaultSingleObjectiveTermination()
 
     def _initialize_advance(self, infills=None, **kwargs):
@@ -125,7 +128,7 @@ def _advance(self, infills=None, **kwargs):
 
         # One-to-one replacement survival
         self.pop = self.survival.do(self.problem, self.pop, infills)
-    
+
     def _set_optimum(self, **kwargs):
         if not has_feasible(self.pop):
             self.opt = self.pop[[np.argmin(self.pop.get("CV"))]]
@@ -134,7 +137,7 @@ def _set_optimum(self, **kwargs):
 
 
 class MODE(DifferentialEvolution):
-    
+
     def __init__(self,
                  pop_size=100,
                  sampling=LHS(),
@@ -146,7 +149,7 @@ def __init__(self,
                  survival=RankAndCrowding(),
                  output=MultiObjectiveOutput(),
                  **kwargs):
-        
+
         super().__init__(
             pop_size=pop_size,
             sampling=sampling,
@@ -159,8 +162,8 @@ def __init__(self,
             output=output,
             **kwargs,
         )
-        
+
         self.termination = DefaultMultiObjectiveTermination()
-    
+
     def _initialize_advance(self, infills=None, **kwargs):
         self.pop = self.survival.do(self.problem, infills, None, n_survive=self.pop_size)

pymoode/algorithms/base/evolutionary.py

@@ -1,10 +1,16 @@
+# Native
+from typing import Optional
+
 # pymoo imports
 from pymoo.core.algorithm import Algorithm
 from pymoo.core.duplicate import DefaultDuplicateElimination, NoDuplicateElimination
 from pymoo.core.initialization import Initialization
 from pymoo.core.infill import InfillCriterion
 from pymoo.core.population import Population
-from pymoo.core.repair import NoRepair
+from pymoo.core.repair import NoRepair, Repair
+from pymoo.core.survival import Survival
+from pymoo.core.sampling import Sampling
+from pymoo.core.mating import Mating
 
 
 # =========================================================================================================
@@ -13,42 +19,44 @@
 
 class EvolutionaryAlgorithm(Algorithm):
 
-    def __init__(self,
-                 pop_size=None,
-                 sampling=None,
-                 mating=None,
-                 survival=None,
-                 n_offsprings=None,
-                 eliminate_duplicates=True,
-                 repair=None,
-                 advance_after_initial_infill=False,
-                 **kwargs):
+    def __init__(
+        self,
+        pop_size: Optional[int] = None,
+        sampling: Optional[Sampling] = None,
+        mating: Optional[Mating] = None,
+        survival: Optional[Survival] = None,
+        n_offsprings: Optional[int] = None,
+        eliminate_duplicates: bool = True,
+        repair: Optional[Repair] = None,
+        advance_after_initial_infill: bool = False,
+        **kwargs
+    ):
         """Base class for Evolutionary Algorithms
 
         Parameters
         ----------
         pop_size : int, optional
             Population size, by default None
-        
+
         sampling : Sampling, optional
             pymoo Sampling instance, by default None
-        
+
         mating : InfillCriterion, optional
             pymoo mating operator, by default None
-        
+
         survival : Survival, optional
             pymoo survival operator, by default None
-        
+
         n_offsprings : int, optional
             Number of offspring individuals created at each generation, by default None
-        
+
         eliminate_duplicates : DuplicateElimination | bool | None, optional
             Eliminate duplicates in mating, by default True
-        
+
         repair : Repair, optional
             pymoo repair operator. In the algorithm level it should be called when sampling.
             It is recommended that mating operators also have some repair associated with. By default None
-        
+
         advance_after_initial_infill : bool, optional
             Either or not apply survival after initialization, by default False
         """

pymoode/algorithms/nsde.py

@@ -11,7 +11,7 @@
 # =========================================================================================================
 
 class NSDE(MODE):
-    
+
     def __init__(self,
                  pop_size=100,
                  variant="DE/rand/1/bin",
@@ -22,7 +22,7 @@ def __init__(self,
                  survival=RankAndCrowding(),
                  **kwargs):
         """
-        NSDE is an algorithm that combines that combines NSGA-II sorting and survival strategies 
+        NSDE is an algorithm that combines that combines NSGA-II sorting and survival strategies
         to DE mutation and crossover.
 
         For many-objective problems, try using NSDE-R, GDE3-MNN, or GDE3-2NN.
@@ -93,7 +93,7 @@ def __init__(self,
             survival=survival,
             **kwargs,
         )
-        
+
     def _advance(self, infills=None, **kwargs):
 
         assert infills is not None, "This algorithms uses the AskAndTell interface thus 'infills' must to be provided."