Crylab
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diff --git a/‎example2.py
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diff --git a/‎pygad/.DS_Store
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diff --git a/‎pygad/helper/.DS_Store
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diff --git a/‎pygad/helper/__pycache__/misc.cpython-310.pyc
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diff --git a/‎pygad/helper/__pycache__/unique.cpython-310.pyc
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diff --git a/‎pygad/helper/misc.py
Lines changed: 6 additions & 4 deletions b/‎pygad/helper/misc.py
Lines changed: 6 additions & 4 deletions
@@ -28,12 +28,12 @@ def fitness_func(ga_instance, solution, solution_idx):
                        allow_duplicate_genes=False,
                        # gene_space=numpy.unique(numpy.random.uniform(1, 100, size=100)),
                        gene_space=[range(0, 100), {"low": 0, "high": 100, 'step': 1}, 2.5891221, [1,2,3,4], None, numpy.unique(numpy.random.uniform(1, 100, size=4))],
-                       gene_constraint=[lambda x: x[0]>=95,lambda x: x[1]>=95,lambda x: x[2]<98,lambda x: x[3]<98,lambda x: x[4]<98,lambda x: x[5]<98],
+                       gene_constraint=[lambda x: x[0]>=70,lambda x: x[1]>=70,lambda x: x[2]<98,lambda x: x[3]<98,lambda x: x[4]<98,lambda x: x[5]<98],
                        )
 
 print(ga_instance.initial_population)
 
-# ga_instance.run()
+ga_instance.run()
 
 # print(ga_instance.gene_space_unpacked)
 # print(ga_instance.population)
@@ -19,7 +19,7 @@ def change_population_dtype_and_round(self,
         It returns the iterable with the data type changed for all genes.
         """
 
-        population_new = population.copy()
+        population_new = numpy.array(population.copy(), dtype=object)
 
         # Forcing the iterable to have the data type assigned to the gene_type parameter.
         if self.gene_type_single == True:
@@ -49,8 +49,10 @@ def change_population_dtype_and_round(self,
                     population_new[:, gene_idx] = numpy.round(numpy.array(population[:, gene_idx], float),
                                                               self.gene_type[gene_idx][1])
                 # Once rounding is done, change the data type.
-                population_new[:, gene_idx] = numpy.asarray(population_new[:, gene_idx],
-                                                            dtype=self.gene_type[gene_idx][0])
+                # population_new[:, gene_idx] = numpy.asarray(population_new[:, gene_idx], dtype=self.gene_type[gene_idx][0])
+                # Use a for loop to maintain the data type of each individual gene.
+                for sol_idx in range(population.shape[0]):
+                    population_new[sol_idx, gene_idx] = self.gene_type[gene_idx][0](population_new[sol_idx, gene_idx])
         return population_new
 
     def change_gene_dtype_and_round(self,
@@ -153,7 +155,7 @@ def filter_gene_values_by_constraint(self,
         else:
             # No value found for the current gene that satisfies the constraint.
             if not self.suppress_warnings:
-                warnings.warn(f"No value found for the gene at index {gene_idx} that satisfies its gene constraint.")
+                warnings.warn(f"No value found for the gene at index {gene_idx} with value {solution[gene_idx]} that satisfies its gene constraint.")
             return None
 
         filtered_values = values[filtered_values_indices]