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Commit9422c1a

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Fix bug when save_best_solution=True
The bug is fixed by creating a copy of the best solution. This is by replacing the next line:```pythonbest_solution = self.population[best_match_idx, :]```By this line:```pythonbest_solution = self.population[best_match_idx, :].copy()```
1 parentc7dd751 commit9422c1a

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+14
-13
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1 file changed

+14
-13
lines changed

‎pygad.py

Lines changed: 14 additions & 13 deletions
Original file line numberDiff line numberDiff line change
@@ -661,9 +661,9 @@ def initialize_population(self, low, high):
661661
self.gene_space[gene_idx]=numpy.asarray(numpy.random.uniform(low=low,
662662
high=high,
663663
size=1),dtype=self.gene_type)[0]
664-
self.population[sol_idx,gene_idx]=self.gene_space[gene_idx]
664+
self.population[sol_idx,gene_idx]=self.gene_space[gene_idx].copy()
665665
eliftype(self.gene_space[gene_idx])in [int,float,numpy.int,numpy.int8,numpy.int16,numpy.int32,numpy.int64,numpy.uint,numpy.uint8,numpy.uint16,numpy.uint32,numpy.uint64,numpy.float,numpy.float16,numpy.float32,numpy.float64]:
666-
self.population[sol_idx,gene_idx]=self.gene_space[gene_idx]
666+
self.population[sol_idx,gene_idx]=self.gene_space[gene_idx].copy()
667667
else:
668668
# Replace all the None values with random values using the init_range_low, init_range_high, and gene_type attributes.
669669
foridx,curr_gene_spaceinenumerate(self.gene_space):
@@ -727,6 +727,7 @@ def run(self):
727727
# Appending the best solution to the best_solutions list.
728728
ifself.save_best_solutions:
729729
self.best_solutions.append(best_solution)
730+
print("AAAAAAAAA ",self.best_solutions)
730731

731732
# Selecting the best parents in the population for mating.
732733
parents=self.select_parents(fitness,num_parents=self.num_parents_mating)
@@ -809,7 +810,7 @@ def steady_state_selection(self, fitness, num_parents):
809810
# Selecting the best individuals in the current generation as parents for producing the offspring of the next generation.
810811
parents=numpy.empty((num_parents,self.population.shape[1]))
811812
forparent_numinrange(num_parents):
812-
parents[parent_num, :]=self.population[fitness_sorted[parent_num], :]
813+
parents[parent_num, :]=self.population[fitness_sorted[parent_num], :].copy()
813814
returnparents
814815

815816
defrank_selection(self,fitness,num_parents):
@@ -827,7 +828,7 @@ def rank_selection(self, fitness, num_parents):
827828
# Selecting the best individuals in the current generation as parents for producing the offspring of the next generation.
828829
parents=numpy.empty((num_parents,self.population.shape[1]))
829830
forparent_numinrange(num_parents):
830-
parents[parent_num, :]=self.population[fitness_sorted[parent_num], :]
831+
parents[parent_num, :]=self.population[fitness_sorted[parent_num], :].copy()
831832
returnparents
832833

833834
defrandom_selection(self,fitness,num_parents):
@@ -845,7 +846,7 @@ def random_selection(self, fitness, num_parents):
845846
rand_indices=numpy.random.randint(low=0.0,high=fitness.shape[0],size=num_parents)
846847

847848
forparent_numinrange(num_parents):
848-
parents[parent_num, :]=self.population[rand_indices[parent_num], :]
849+
parents[parent_num, :]=self.population[rand_indices[parent_num], :].copy()
849850
returnparents
850851

851852
deftournament_selection(self,fitness,num_parents):
@@ -863,7 +864,7 @@ def tournament_selection(self, fitness, num_parents):
863864
rand_indices=numpy.random.randint(low=0.0,high=len(fitness),size=self.K_tournament)
864865
K_fitnesses=fitness[rand_indices]
865866
selected_parent_idx=numpy.where(K_fitnesses==numpy.max(K_fitnesses))[0][0]
866-
parents[parent_num, :]=self.population[rand_indices[selected_parent_idx], :]
867+
parents[parent_num, :]=self.population[rand_indices[selected_parent_idx], :].copy()
867868
returnparents
868869

869870
defroulette_wheel_selection(self,fitness,num_parents):
@@ -897,7 +898,7 @@ def roulette_wheel_selection(self, fitness, num_parents):
897898
rand_prob=numpy.random.rand()
898899
foridxinrange(probs.shape[0]):
899900
if (rand_prob>=probs_start[idx]andrand_prob<probs_end[idx]):
900-
parents[parent_num, :]=self.population[idx, :]
901+
parents[parent_num, :]=self.population[idx, :].copy()
901902
break
902903
returnparents
903904

@@ -935,7 +936,7 @@ def stochastic_universal_selection(self, fitness, num_parents):
935936
rand_pointer=first_pointer+parent_num*pointers_distance
936937
foridxinrange(probs.shape[0]):
937938
if (rand_pointer>=probs_start[idx]andrand_pointer<probs_end[idx]):
938-
parents[parent_num, :]=self.population[idx, :]
939+
parents[parent_num, :]=self.population[idx, :].copy()
939940
break
940941
returnparents
941942

@@ -1161,7 +1162,7 @@ def mutation_by_space(self, offspring):
11611162

11621163
ifself.gene_space_nested:
11631164
# Returning the current gene space from the 'gene_space' attribute.
1164-
curr_gene_space=self.gene_space[gene_idx]
1165+
curr_gene_space=self.gene_space[gene_idx].copy()
11651166

11661167
# If the gene space has only a single value, use it as the new gene value.
11671168
iftype(curr_gene_space)in [int,float,numpy.int,numpy.int8,numpy.int16,numpy.int32,numpy.int64,numpy.uint,numpy.uint8,numpy.uint16,numpy.uint32,numpy.uint64,numpy.float,numpy.float16,numpy.float32,numpy.float64]:
@@ -1203,7 +1204,7 @@ def mutation_probs_by_space(self, offspring):
12031204
ifprobs[gene_idx]<=self.mutation_probability:
12041205
ifself.gene_space_nested:
12051206
# Returning the current gene space from the 'gene_space' attribute.
1206-
curr_gene_space=self.gene_space[gene_idx]
1207+
curr_gene_space=self.gene_space[gene_idx].copy()
12071208

12081209
# If the gene space has only a single value, use it as the new gene value.
12091210
iftype(curr_gene_space)in [int,float,numpy.int,numpy.int8,numpy.int16,numpy.int32,numpy.int64,numpy.uint,numpy.uint8,numpy.uint16,numpy.uint32,numpy.uint64,numpy.float,numpy.float16,numpy.float32,numpy.float64]:
@@ -1422,7 +1423,7 @@ def adaptive_mutation_by_space(self, offspring):
14221423

14231424
ifself.gene_space_nested:
14241425
# Returning the current gene space from the 'gene_space' attribute.
1425-
curr_gene_space=self.gene_space[gene_idx]
1426+
curr_gene_space=self.gene_space[gene_idx].copy()
14261427

14271428
# If the gene space has only a single value, use it as the new gene value.
14281429
iftype(curr_gene_space)in [int,float,numpy.int,numpy.int8,numpy.int16,numpy.int32,numpy.int64,numpy.uint,numpy.uint8,numpy.uint16,numpy.uint32,numpy.uint64,numpy.float,numpy.float16,numpy.float32,numpy.float64]:
@@ -1510,7 +1511,7 @@ def adaptive_mutation_probs_by_space(self, offspring):
15101511
ifprobs[gene_idx]<=adaptive_mutation_probability:
15111512
ifself.gene_space_nested:
15121513
# Returning the current gene space from the 'gene_space' attribute.
1513-
curr_gene_space=self.gene_space[gene_idx]
1514+
curr_gene_space=self.gene_space[gene_idx].copy()
15141515

15151516
# If the gene space has only a single value, use it as the new gene value.
15161517
iftype(curr_gene_space)in [int,float,numpy.int,numpy.int8,numpy.int16,numpy.int32,numpy.int64,numpy.uint,numpy.uint8,numpy.uint16,numpy.uint32,numpy.uint64,numpy.float,numpy.float16,numpy.float32,numpy.float64]:
@@ -1597,7 +1598,7 @@ def best_solution(self, pop_fitness=None):
15971598
# Then return the index of that solution corresponding to the best fitness.
15981599
best_match_idx=numpy.where(pop_fitness==numpy.max(pop_fitness))[0][0]
15991600

1600-
best_solution=self.population[best_match_idx, :]
1601+
best_solution=self.population[best_match_idx, :].copy()
16011602
best_solution_fitness=pop_fitness[best_match_idx]
16021603

16031604
returnbest_solution,best_solution_fitness,best_match_idx

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