Crylab
diff --git a/‎example2.py
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diff --git a/‎pygad/__pycache__/__init__.cpython-310.pyc
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diff --git a/‎pygad/helper/__pycache__/misc.cpython-310.pyc
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diff --git a/‎pygad/helper/misc.py
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diff --git a/‎pygad/helper/unique.py
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diff --git a/‎pygad/pygad.py
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diff --git a/‎pygad/utils/__pycache__/__init__.cpython-310.pyc
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@@ -0,0 +1,39 @@
+import pygad
+import numpy
+
+function_inputs = [4,-2,3.5,5,-11,-4.7]
+desired_output = 44
+
+def fitness_func(ga_instance, solution, solution_idx):
+    output = numpy.sum(solution*function_inputs)
+    fitness = 1.0 / (numpy.abs(output - desired_output) + 0.000001)
+    return fitness
+
+num_genes = len(function_inputs)
+
+ga_instance = pygad.GA(num_generations=100,
+                       num_parents_mating=10,
+                       sol_per_pop=20,
+                       num_genes=num_genes,
+                       mutation_num_genes=6,
+                       fitness_func=fitness_func,
+                       init_range_low=1,
+                       init_range_high=100,
+                       # suppress_warnings=True,
+                       random_mutation_min_val=1,
+                       random_mutation_max_val=100,
+                       mutation_by_replacement=True,
+                       gene_type=[float, 1],
+                       save_solutions=True,
+                       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],
+                       )
+
+print(ga_instance.initial_population)
+
+# ga_instance.run()
+
+# print(ga_instance.gene_space_unpacked)
+# print(ga_instance.population)
@@ -229,7 +229,12 @@ def generate_gene_value_from_space(self,
             -An array with number of maximum number of values equal to sample_size if sample_size>1.
         """
 
-        range_min, range_max = self.get_random_mutation_range(gene_idx)
+        if gene_value is None:
+            # Use the initial population range.
+            range_min, range_max = self.get_initial_population_range(gene_index=gene_idx)
+        else:
+            # Use the mutation range. 
+            range_min, range_max = self.get_random_mutation_range(gene_idx)
 
         if self.gene_space_nested:
             # Returning the current gene space from the 'gene_space' attribute.