[pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

genetic_algorithm/genetic_algorithm_optimization.py

@@ -1,6 +1,7 @@
 import random
 from collections.abc import Callable, Sequence
 from concurrent.futures import ThreadPoolExecutor
+
 import numpy as np
 
 # Parameters
@@ -59,10 +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)
         ]
 
@@ -82,10 +80,10 @@ class GeneticAlgorithm:
         ... )
         >>> individual = np.array([1.0, 2.0])
         >>> ga.fitness(individual)
-        5.0  # The fitness should be 1^2 + 2^2 = 5
+        -5.0  # The fitness should be -1^2 + 2^2 = 5 for minimizing
         >>> ga.maximize = True
         >>> ga.fitness(individual)
-        -5.0  # The fitness should be -5 when maximizing
+        5.0  # The fitness should be 1^2 + 2^2 = 5 when maximizing
         """
         value = float(self.function(*individual))  # Ensure fitness is a float
         return value if self.maximize else -value  # If minimizing, invert the fitness
@@ -114,11 +112,17 @@ class GeneticAlgorithm:
         >>> selected_parents = ga.select_parents(population_score)
         >>> len(selected_parents)
         2  # Should select the two parents with the best fitness scores.
-        >>> np.array_equal(selected_parents[0], np.array([1.0, 2.0]))  # Parent 1 should be [1.0, 2.0]
+        >>> np.array_equal(selected_parents[0], np.array([1.0, 2.0]))
+        # Parent 1 should be [1.0, 2.0]
         True
-        >>> np.array_equal(selected_parents[1], np.array([-1.0, -2.0]))  # Parent 2 should be [-1.0, -2.0]
+        >>> np.array_equal(selected_parents[1], np.array([-1.0, -2.0]))
+        # 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]]
@@ -237,10 +241,15 @@ class GeneticAlgorithm:
         >>> isinstance(best_solution[1], float)  # Second element should be a float
         True
         """
+        best_individual = None
         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]
@@ -253,7 +262,10 @@ class GeneticAlgorithm:
 
             # 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))