Fix selecting a unique value from gene space

Author: Ahmed Gad

example2.py

@@ -9,14 +9,14 @@ def fitness_func(ga, solution, idx):
                        num_parents_mating=5,
                        sol_per_pop=10,
                        num_genes=10,
-                       random_seed=123,
+                       random_seed=2,
                        # mutation_type=None,
                        # crossover_type=None,
-                       random_mutation_min_val=1,
-                       random_mutation_max_val=100,
+                       # random_mutation_min_val=1,
+                       # random_mutation_max_val=100,
                        fitness_func=fitness_func,
-                       gene_space=[30, None, 40, 50, None, 60, 70, None, None, None],
-                       gene_type=int,
+                       gene_space=[5],
+                       gene_type=float,
                        allow_duplicate_genes=False,
                        save_solutions=True)
 
@@ -25,7 +25,7 @@ def fitness_func(ga, solution, idx):
 ga_instance.run()
 
 # print(ga_instance.gene_space_unpacked)
-# print(ga_instance.population)
+print(ga_instance.population)
 
 """
 gene_space=[[0, 0],

pygad/__pycache__/pygad.cpython-310.pyc

@@ -367,6 +367,8 @@ def generate_gene_value_from_space(self,
         # It has to be called here for all the missed cases.
         value_from_space = self.change_gene_dtype_and_round(gene_index=gene_idx,
                                                             gene_value=value_from_space)
+        if sample_size == 1 and type(value_from_space) not in pygad.GA.supported_int_float_types:
+            value_from_space = value_from_space[0]
 
         return value_from_space
 

pygad/helper/__pycache__/misc.cpython-310.pyc

@@ -1376,7 +1376,7 @@ def initialize_population(self,
             # 4) Solve duplicates if not allowed.
 
         # Create an empty population.
-        self.population = numpy.empty(shape=self.pop_size, dtype=object)
+        self.population = numpy.zeros(shape=self.pop_size, dtype=object)
 
         # 1) Create the initial population either randomly or using the gene space.
         if self.gene_space is None:

pygad/helper/__pycache__/unique.cpython-310.pyc

@@ -31,6 +31,7 @@ def number_respect_gene_space(gene_space=None,
                               init_range_low=-4,
                               init_range_high=4,
                               initial_population=None,
+                              allow_duplicate_genes=False,
                               parent_selection_type='sss',
                               multi_objective=False):
 
@@ -167,7 +168,7 @@ def test_gene_space_dict_with_step():
     assert num_outside == 0
 
 def test_gene_space_list_single_value():
-    num_outside, ga_instance = number_respect_gene_space(gene_space=[5])
+    num_outside, ga_instance = number_respect_gene_space(gene_space=[5], allow_duplicate_genes=True)
 
     assert num_outside == 0