Update genetic_algorithm_optimization.py

Author: night-spring

genetic_algorithm/genetic_algorithm_optimization.py

@@ -1,12 +1,11 @@
-from collections.abc import Callable  # Fixes the UP035 warning
-import numpy as np
-
+from collections.abc import Callable  # Sorted import
+import numpy as np  # Sorted import
 
 class GeneticAlgorithmOptimizer:
     def __init__(
         self,
-        objective_function: Callable[..., float],
-        variable_bounds: list[tuple[float, float]],
+        objective_function: Callable[..., float],  
+        variable_bounds: list[tuple[float, float]],  
         population_size: int = 100,
         max_generations: int = 500,
         crossover_probability: float = 0.9,
@@ -19,9 +18,7 @@ def __init__(
         self.crossover_probability = crossover_probability
         self.mutation_probability = mutation_probability
         self.num_variables = len(variable_bounds)
-
-        # Initialize the random number generator
-        self.rng = np.random.default_rng()
+        self.rng = np.random.default_rng()  # Initialize random generator
 
     def generate_initial_population(self) -> np.ndarray:
         """
@@ -55,19 +52,14 @@ def perform_crossover(
     ) -> tuple[np.ndarray, np.ndarray]:
         """
         Perform one-point crossover between two parents to create offspring.
-        Skip crossover for single-variable functions.
         """
         if self.num_variables == 1:
             return parent1, parent2
 
         if self.rng.random() < self.crossover_probability:
             crossover_point = self.rng.integers(1, self.num_variables)
-            child1 = np.concatenate(
-                (parent1[:crossover_point], parent2[crossover_point:])
-            )
-            child2 = np.concatenate(
-                (parent2[:crossover_point], parent1[crossover_point:])
-            )
+            child1 = np.concatenate((parent1[:crossover_point], parent2[crossover_point:]))
+            child2 = np.concatenate((parent2[:crossover_point], parent1[crossover_point:]))
             return child1, child2
         return parent1, parent2
 
@@ -79,7 +71,7 @@ def apply_mutation(self, individual: np.ndarray) -> np.ndarray:
             mutation_index = self.rng.integers(0, self.num_variables)
             individual[mutation_index] = self.rng.uniform(
                 self.variable_bounds[mutation_index, 0],
-                self.variable_bounds[mutation_index, 1],
+                self.variable_bounds[mutation_index, 1]
             )
         return individual
 
@@ -113,15 +105,11 @@ def optimize(self) -> tuple[np.ndarray, float]:
                 best_fitness_value = fitness_values[min_fitness_index]
                 best_solution = population[min_fitness_index]
 
-            print(
-                f"Generation {generation + 1}, Best Fitness Value: {best_fitness_value}"
-            )
+            print(f"Generation {generation + 1}, Best Fitness Value: {best_fitness_value}")
 
         return best_solution, best_fitness_value
 
-
 if __name__ == "__main__":
-
     def objective_function(x: float, y: float) -> float:
         """
         Example objective function to minimize x^2 + y^2
@@ -131,7 +119,8 @@ def objective_function(x: float, y: float) -> float:
     variable_bounds: list[tuple[float, float]] = [(-10, 10), (-10, 10)]
 
     optimizer = GeneticAlgorithmOptimizer(
-        objective_function=objective_function, variable_bounds=variable_bounds
+        objective_function=objective_function, 
+        variable_bounds=variable_bounds
     )
     best_solution, best_fitness_value = optimizer.optimize()