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Create genetic_algorithm_optimization.py
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UTSAV SINGHAL
GitHub
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genetic_algorithm/genetic_algorithm_optimization.py
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214
genetic_algorithm/genetic_algorithm_optimization.py
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import random
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from collections.abc import Callable, Sequence
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from concurrent.futures import ThreadPoolExecutor
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import numpy as np
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# Parameters
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N_POPULATION = 100 # Population size
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N_GENERATIONS = 500 # Maximum number of generations
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N_SELECTED = 50 # Number of parents selected for the next generation
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MUTATION_PROBABILITY = 0.1 # Mutation probability
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CROSSOVER_RATE = 0.8 # Probability of crossover
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SEARCH_SPACE = (-10, 10) # Search space for the variables
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# Random number generator
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rng = np.random.default_rng()
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class GeneticAlgorithm:
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def __init__(
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self,
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function: Callable[[float, float], float],
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bounds: Sequence[tuple[int | float, int | float]],
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population_size: int,
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generations: int,
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mutation_prob: float,
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crossover_rate: float,
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maximize: bool = True,
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) -> None:
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self.function = function # Target function to optimize
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self.bounds = bounds # Search space bounds (for each variable)
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self.population_size = population_size
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self.generations = generations
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self.mutation_prob = mutation_prob
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self.crossover_rate = crossover_rate
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self.maximize = maximize
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self.dim = len(bounds) # Dimensionality of the function (number of variables)
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# Initialize population
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self.population = self.initialize_population()
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def initialize_population(self) -> list[np.ndarray]:
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"""Initialize the population with random individuals within the search space."""
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return [
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rng.uniform(
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low=[self.bounds[j][0] for j in range(self.dim)],
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high=[self.bounds[j][1] for j in range(self.dim)],
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)
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for _ in range(self.population_size)
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]
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def fitness(self, individual: np.ndarray) -> float:
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"""Calculate the fitness value (function value) for an individual."""
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value = float(self.function(*individual)) # Ensure fitness is a float
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return value if self.maximize else -value # If minimizing, invert the fitness
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def select_parents(
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self, population_score: list[tuple[np.ndarray, float]]
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) -> list[np.ndarray]:
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"""Select top N_SELECTED parents based on fitness."""
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population_score.sort(key=lambda score_tuple: score_tuple[1], reverse=True)
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selected_count = min(N_SELECTED, len(population_score))
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return [ind for ind, _ in population_score[:selected_count]]
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def crossover(
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self, parent1: np.ndarray, parent2: np.ndarray
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) -> tuple[np.ndarray, np.ndarray]:
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"""
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Perform uniform crossover between two parents to generate offspring.
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Args:
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parent1 (np.ndarray): The first parent.
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parent2 (np.ndarray): The second parent.
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Returns:
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tuple[np.ndarray, np.ndarray]: The two offspring generated by crossover.
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Example:
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>>> ga = GeneticAlgorithm(
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... lambda x, y: -(x**2 + y**2),
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... [(-10, 10), (-10, 10)],
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... 10, 100, 0.1, 0.8, True
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... )
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>>> parent1, parent2 = np.array([1, 2]), np.array([3, 4])
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>>> len(ga.crossover(parent1, parent2)) == 2
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True
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"""
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if random.random() < self.crossover_rate:
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cross_point = random.randint(1, self.dim - 1)
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child1 = np.concatenate((parent1[:cross_point], parent2[cross_point:]))
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child2 = np.concatenate((parent2[:cross_point], parent1[cross_point:]))
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return child1, child2
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return parent1, parent2
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def mutate(self, individual: np.ndarray) -> np.ndarray:
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"""
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Apply mutation to an individual.
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Args:
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individual (np.ndarray): The individual to mutate.
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Returns:
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np.ndarray: The mutated individual.
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Example:
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>>> ga = GeneticAlgorithm(
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... lambda x, y: -(x**2 + y**2),
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... [(-10, 10), (-10, 10)],
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... 10, 100, 0.1, 0.8, True
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... )
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>>> ind = np.array([1.0, 2.0])
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>>> mutated = ga.mutate(ind)
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>>> len(mutated) == 2 # Ensure it still has the correct number of dimensions
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True
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"""
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for i in range(self.dim):
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if random.random() < self.mutation_prob:
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individual[i] = rng.uniform(self.bounds[i][0], self.bounds[i][1])
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return individual
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def evaluate_population(self) -> list[tuple[np.ndarray, float]]:
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"""
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Evaluate the fitness of the entire population in parallel.
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Returns:
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list[tuple[np.ndarray, float]]:
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The population with their respective fitness values.
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Example:
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>>> ga = GeneticAlgorithm(
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... lambda x, y: -(x**2 + y**2),
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... [(-10, 10), (-10, 10)],
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... 10, 100, 0.1, 0.8, True
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... )
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>>> eval_population = ga.evaluate_population()
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>>> len(eval_population) == ga.population_size # Ensure population size
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True
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>>> all(
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... isinstance(ind, tuple) and isinstance(ind[1], float)
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... for ind in eval_population
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... )
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True
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"""
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with ThreadPoolExecutor() as executor:
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return list(
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executor.map(
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lambda individual: (individual, self.fitness(individual)),
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self.population,
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)
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)
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def evolve(self, verbose=True) -> np.ndarray:
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"""
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Evolve the population over the generations to find the best solution.
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Returns:
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np.ndarray: The best individual found during the evolution process.
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"""
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for generation in range(self.generations):
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# Evaluate population fitness (multithreaded)
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population_score = self.evaluate_population()
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# Check the best individual
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best_individual = max(
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population_score, key=lambda score_tuple: score_tuple[1]
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)[0]
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best_fitness = self.fitness(best_individual)
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# Select parents for next generation
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parents = self.select_parents(population_score)
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next_generation = []
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# Generate offspring using crossover and mutation
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for i in range(0, len(parents), 2):
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parent1, parent2 = parents[i], parents[(i + 1) % len(parents)]
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child1, child2 = self.crossover(parent1, parent2)
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next_generation.append(self.mutate(child1))
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next_generation.append(self.mutate(child2))
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# Ensure population size remains the same
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self.population = next_generation[: self.population_size]
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if verbose and generation % 10 == 0:
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print(f"Generation {generation}: Best Fitness = {best_fitness}")
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return best_individual
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# Example target function for optimization
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def target_function(var_x: float, var_y: float) -> float:
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"""
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Example target function (parabola) for optimization.
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Args:
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var_x (float): The x-coordinate.
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var_y (float): The y-coordinate.
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Returns:
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float: The value of the function at (var_x, var_y).
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Example:
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>>> target_function(0, 0)
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0
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>>> target_function(1, 1)
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2
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"""
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return var_x**2 + var_y**2 # Simple parabolic surface (minimization)
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# Set bounds for the variables (var_x, var_y)
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bounds = [(-10, 10), (-10, 10)] # Both var_x and var_y range from -10 to 10
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# Instantiate and run the genetic algorithm
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ga = GeneticAlgorithm(
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function=target_function,
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bounds=bounds,
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population_size=N_POPULATION,
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generations=N_GENERATIONS,
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mutation_prob=MUTATION_PROBABILITY,
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crossover_rate=CROSSOVER_RATE,
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maximize=False, # Minimize the function
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)
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best_solution = ga.evolve()
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print(f"Best solution found: {best_solution}")
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print(f"Best fitness (minimum value of function): {target_function(*best_solution)}")
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