Update genetic_algorithm_optimization.py

Author: UTSAVS26

genetic_algorithm/genetic_algorithm_optimization.py

@@ -2,6 +2,7 @@
 import random
 from concurrent.futures import ThreadPoolExecutor
 
+
 # Parameters
 N_POPULATION = 100  # Population size
 N_GENERATIONS = 500  # Maximum number of generations
@@ -10,6 +11,7 @@
 CROSSOVER_RATE = 0.8  # Probability of crossover
 SEARCH_SPACE = (-10, 10)  # Search space for the variables
 
+
 # Random number generator
 rng = np.random.default_rng()
 
@@ -25,6 +27,7 @@ def __init__(
         crossover_rate,
         maximize=True,
     ):
+
         self.function = function  # Target function to optimize
         self.bounds = bounds  # Search space bounds (for each variable)
         self.population_size = population_size
@@ -44,16 +47,19 @@ def initialize_population(self):
             for i in range(self.population_size)
         ]
 
+
     def fitness(self, individual):
         # Calculate the fitness value (function value)
         value = self.function(*individual)
         return value if self.maximize else -value  # If minimizing, invert the fitness
 
+
     def select_parents(self, population_score):
         # Select top N_SELECTED parents based on fitness
         population_score.sort(key=lambda x: x[1], reverse=True)
         return [ind for ind, _ in population_score[:N_SELECTED]]
 
+
     def crossover(self, parent1, parent2):
         # Perform uniform crossover
         if random.random() < self.crossover_rate:
@@ -63,20 +69,23 @@ def crossover(self, parent1, parent2):
             return child1, child2
         return parent1, parent2
 
+
     def mutate(self, individual):
         # Apply mutation to an individual using the new random generator
         for i in range(self.dim):
             if random.random() < self.mutation_prob:
                 individual[i] = rng.uniform(self.bounds[i][0], self.bounds[i][1])
         return individual
 
+
     def evaluate_population(self):
         # Multithreaded evaluation of population fitness
         with ThreadPoolExecutor() as executor:
             return list(
                 executor.map(lambda ind: (ind, self.fitness(ind)), self.population)
             )
 
+
     def evolve(self):
         for generation in range(self.generations):
             # Evaluate population fitness (multithreaded)
@@ -114,6 +123,7 @@ def target_function(x, y):
 # Set bounds for the variables (x, y)
 bounds = [(-10, 10), (-10, 10)]  # Both x and y range from -10 to 10
 
+
 # Instantiate and run the genetic algorithm
 ga = GeneticAlgorithm(
     function=target_function,
@@ -125,6 +135,7 @@ def target_function(x, y):
     maximize=False,  # Minimize the function
 )
 
+
 best_solution = ga.evolve()
 print(f"Best solution found: {best_solution}")
 print(f"Best fitness (minimum value of function): {target_function(*best_solution)}")