diff --git a/genetic_algorithm/genetic_algorithm_optimization.py b/genetic_algorithm/genetic_algorithm_optimization.py
index 6a1020aa7..813ac72e8 100644
--- a/genetic_algorithm/genetic_algorithm_optimization.py
+++ b/genetic_algorithm/genetic_algorithm_optimization.py
@@ -60,10 +60,7 @@ class GeneticAlgorithm:
         True
         """
         return [
-            rng.uniform(
-                low=[self.bounds[j][0] for j in range(self.dim)],
-                high=[self.bounds[j][1] for j in range(self.dim)],
-            )
+            np.array([rng.uniform(b[0], b[1]) for b in self.bounds])
             for _ in range(self.population_size)
         ]
 
@@ -122,6 +119,10 @@ class GeneticAlgorithm:
         # Parent 2 should be [-1.0, -2.0]
         True
         """
+
+        if not population_score:
+            raise ValueError("Population score is empty, cannot select parents.")
+        
         population_score.sort(key=lambda score_tuple: score_tuple[1], reverse=True)
         selected_count = min(N_SELECTED, len(population_score))
         return [ind for ind, _ in population_score[:selected_count]]
@@ -244,30 +245,32 @@ class GeneticAlgorithm:
         for generation in range(self.generations):
             # Evaluate population fitness (multithreaded)
             population_score = self.evaluate_population()
-
+    
+            # Ensure population_score isn't empty
+            if not population_score:
+                raise ValueError("Population score is empty. No individuals evaluated.")
+    
             # Check the best individual
-            best_individual = max(
-                population_score, key=lambda score_tuple: score_tuple[1]
-            )[0]
+            best_individual = max(population_score, key=lambda score_tuple: score_tuple[1])[0]
             best_fitness = self.fitness(best_individual)
-
+    
             # Select parents for next generation
             parents = self.select_parents(population_score)
             next_generation = []
-
+    
             # Generate offspring using crossover and mutation
             for i in range(0, len(parents), 2):
-                parent1, parent2 = parents[i], parents[(i + 1) % len(parents)]
+                parent1, parent2 = parents[i], parents[(i + 1) % len(parents)]  # Wrap around for odd cases
                 child1, child2 = self.crossover(parent1, parent2)
                 next_generation.append(self.mutate(child1))
                 next_generation.append(self.mutate(child2))
-
+    
             # Ensure population size remains the same
             self.population = next_generation[: self.population_size]
-
+    
             if verbose and generation % 10 == 0:
                 print(f"Generation {generation}: Best Fitness = {best_fitness}")
-
+    
         return best_individual