make tournament create new objects for chromosomes + tests

as we want to create new population and because all of out methods are by reference, creating new instances from tournament is the best way to handle any further misuse.

Took 1 hour 21 minutes

tests/test_utils.py

@@ -318,16 +318,53 @@ def test_fittest_chromosome(supply_population):
 def test_tournament(supply_population):
     parents = F.tournament(supply_population)
     assert parents.len() == 2
-    assert supply_population.contains(parents[0]) == True
-    assert supply_population.contains(parents[1]) == True
+
+    f = 0
+    idcs = [-1, -1]
+    for idx, ch in enumerate(supply_population):
+        if ch.id == parents[0].id and ch.fitness_value() == parents[0].fitness_value():
+            f += 1
+            idcs[0] = idx
+        if ch.id == parents[1].id and ch.fitness_value() == parents[1].fitness_value():
+            f += 1
+            idcs[1] = idx
+    assert f >= 2
     assert parents[0] != parents[1]
 
+    for p, ch in zip([p for p in parents], [supply_population[idcs[0]], supply_population[idcs[1]]]):
+        assert p.id == ch.id
+
+    assert parents[0].chromosome[0].x == supply_population[idcs[0]].chromosome[0].x
+    assert parents[0].chromosome[0].y == supply_population[idcs[0]].chromosome[0].y
+
+    assert parents[1].chromosome[0].x == supply_population[idcs[1]].chromosome[0].x
+    assert parents[1].chromosome[0].y == supply_population[idcs[1]].chromosome[0].y
+
     parents = F.tournament(supply_population, 0.0)  # force to use random parents not fittest ('else' condition)
     assert parents.len() == 2
-    assert supply_population.contains(parents[0]) == True
-    assert supply_population.contains(parents[1]) == True
     assert parents[0] != parents[1]
 
+    f = 0
+    idcs = [-1, -1]
+    for idx, ch in enumerate(supply_population):
+        if ch.id == parents[0].id and ch.fitness_value() == parents[0].fitness_value():
+            f += 1
+            idcs[0] = idx
+        if ch.id == parents[1].id and ch.fitness_value() == parents[1].fitness_value():
+            f += 1
+            idcs[1] = idx
+    assert f >= 2
+    assert parents[0] != parents[1]
+
+    for p, ch in zip([p for p in parents], [supply_population[idcs[0]], supply_population[idcs[1]]]):
+        assert p.id == ch.id
+
+    assert parents[0].chromosome[0].x == supply_population[idcs[0]].chromosome[0].x
+    assert parents[0].chromosome[0].y == supply_population[idcs[0]].chromosome[0].y
+
+    assert parents[1].chromosome[0].x == supply_population[idcs[1]].chromosome[0].x
+    assert parents[1].chromosome[0].y == supply_population[idcs[1]].chromosome[0].y
+
 
 def test_routes_ending_indices_attr(supply_depot: Depot):
     F.initial_routing(supply_depot)

utils/functional.py

@@ -122,24 +122,43 @@ def tournament(population: Population, tournament_probability: float = 0.8, size
     3. Select another random unique sample from the population
     4. Find fittest chromosomes from each sampled populations and return them as new population.
     5. Randomly choose two chromosomes and return them as a new population.
+    Note: if samples be same, then fittest `chromosome` of the samples both will be same `Chromosome`s. So as we want to
+        pass the result of `tournament` to `cross_over`, the cross over operation is asexual or single-parent.
 
     :param population: An instance of `Population` class
     :param tournament_probability: The probability of using fittest or random sample (=0.8)
-    :param size: The size of population to be sampled. By default, we use Binary tournament (size = 2).
+    :param size: The size of population to be sampled. By default, we use Binary tournament.
     :return: A `Population` with size of `size`
     """
 
+    # we create new objects to make sure asexual can happen too. (all methods are by reference)
+    first_fittest = Chromosome(7770, -1)
+    second_fittest = Chromosome(7771, -1)
+
     first_sample = extract_population(population, size)
     if random.random() <= tournament_probability:
         second_sample = extract_population(population, size)
-        first_fittest = fittest_chromosome(first_sample)
-        second_fittest = fittest_chromosome(second_sample)
-        while first_sample == second_fittest:
-            second_fittest = fittest_chromosome(second_sample)
+        first = fittest_chromosome(first_sample)
+        second = fittest_chromosome(second_sample)
+        for new, found in zip([first_fittest, second_fittest], [first, second]):
+            new.chromosome = found.get_all()
+            new.id = found.id
+            new.fitness = found.fitness
+            new.capacity = found.capacity
+            new.size = found.size
+
         return Population(0, [first_fittest, second_fittest])
     else:
         indices = random.sample(range(0, first_sample.len()), 2)
-        return Population(0, [first_sample[indices[0]], first_sample[indices[1]]])
+        first = first_sample[indices[0]]
+        second = first_sample[indices[1]]
+        for new, found in zip([first_fittest, second_fittest], [first, second]):
+            new.chromosome = found.get_all()
+            new.id = found.id
+            new.fitness = found.fitness
+            new.capacity = found.capacity
+            new.size = found.size
+        return Population(0, [first_fittest, second_fittest])
 
 
 def extract_random_route(chromosome: Chromosome, delete=True) -> (List[Customer], int, int, int):