siu-issiki/genetic_algorithm.py

## genetic_algorithm.py
import random
import string

TARGET = "Hello, World!"
POPULATION_SIZE = 100
MUTATION_RATE = 0.05
GENERATIONS = 1000
INITIAL_INDIVIDUAL = "Cr3@tiv3_#ode"


def generate_individual():
    return INITIAL_INDIVIDUAL


def calculate_fitness(individual):
    return sum(1 for a, b in zip(individual, TARGET) if a == b) / len(TARGET)


def select(population, fitnesses):
    return random.choices(population, weights=fitnesses, k=2)


def crossover(parent1, parent2):
    crossover_point = random.randint(0, len(TARGET))
    return (
        parent1[:crossover_point] + parent2[crossover_point:],
        parent2[:crossover_point] + parent1[crossover_point:]
    )


def mutate(individual):
    chars = string.ascii_letters + string.punctuation + string.whitespace
    return ''.join(
        c if random.random() > MUTATION_RATE else random.choice(chars)
        for c in individual
    )


population = [generate_individual() for _ in range(POPULATION_SIZE)]

for generation in range(GENERATIONS):
    fitnesses = [calculate_fitness(individual) for individual in population]
    new_population = []

    for _ in range(POPULATION_SIZE // 2):
        parent1, parent2 = select(population, fitnesses)
        child1, child2 = crossover(parent1, parent2)
        new_population.extend([mutate(child1), mutate(child2)])

    population = new_population
    best_individual = max(population, key=calculate_fitness)
    print(f"Generation {generation + 1}, best individual: {best_individual}, "
          f"fitness: {calculate_fitness(best_individual)}")

    if best_individual == TARGET:
        break
	import random
	import string

	TARGET = "Hello, World!"
	POPULATION_SIZE = 100
	MUTATION_RATE = 0.05
	GENERATIONS = 1000
	INITIAL_INDIVIDUAL = "Cr3@tiv3_#ode"


	def generate_individual():
	return INITIAL_INDIVIDUAL


	def calculate_fitness(individual):
	return sum(1 for a, b in zip(individual, TARGET) if a == b) / len(TARGET)


	def select(population, fitnesses):
	return random.choices(population, weights=fitnesses, k=2)


	def crossover(parent1, parent2):
	crossover_point = random.randint(0, len(TARGET))
	return (
	parent1[:crossover_point] + parent2[crossover_point:],
	parent2[:crossover_point] + parent1[crossover_point:]
	)


	def mutate(individual):
	chars = string.ascii_letters + string.punctuation + string.whitespace
	return ''.join(
	c if random.random() > MUTATION_RATE else random.choice(chars)
	for c in individual
	)


	population = [generate_individual() for _ in range(POPULATION_SIZE)]

	for generation in range(GENERATIONS):
	fitnesses = [calculate_fitness(individual) for individual in population]
	new_population = []

	for _ in range(POPULATION_SIZE // 2):
	parent1, parent2 = select(population, fitnesses)
	child1, child2 = crossover(parent1, parent2)
	new_population.extend([mutate(child1), mutate(child2)])

	population = new_population
	best_individual = max(population, key=calculate_fitness)
	print(f"Generation {generation + 1}, best individual: {best_individual}, "
	f"fitness: {calculate_fitness(best_individual)}")

	if best_individual == TARGET:
	break