给我Python实现的遗传算法代码

以下是一个简单的 Python 遗传算法实现，用于最大化一个函数的值：

import random

# 定义函数，这里以求 x^2 的最大值为例
def fitness(x):
    return x * x

# 初始化种群
def init_population(population_size, chromosome_length):
    population = []
    for i in range(population_size):
        chromosome = []
        for j in range(chromosome_length):
            chromosome.append(random.randint(0, 1))
        population.append(chromosome)
    return population

# 计算适应度
def calculate_fitness(population):
    fitness_scores = []
    for chromosome in population:
        x = 0
        for i in range(len(chromosome)):
            x += chromosome[i] * pow(2, i)
        fitness_scores.append(fitness(x))
    return fitness_scores

# 选择
def select(population, fitness_scores):
    selected_population = []
    for i in range(len(population)):
        parent1 = random.randint(0, len(population) - 1)
        parent2 = random.randint(0, len(population) - 1)
        if fitness_scores[parent1] > fitness_scores[parent2]:
            selected_population.append(population[parent1])
        else:
            selected_population.append(population[parent2])
    return selected_population

# 交叉
def crossover(selected_population, crossover_rate):
    for i in range(0, len(selected_population), 2):
        if random.random() < crossover_rate:
            crossover_point = random.randint(0, len(selected_population[i]) - 1)
            temp = selected_population[i][crossover_point:]
            selected_population[i][crossover_point:] = selected_population[i+1][crossover_point:]
            selected_population[i+1][crossover_point:] = temp
    return selected_population

# 变异
def mutate(selected_population, mutation_rate):
    for i in range(len(selected_population)):
        for j in range(len(selected_population[i])):
            if random.random() < mutation_rate:
                selected_population[i][j] = 1 - selected_population[i][j]
    return selected_population

# 遗传算法主函数
def genetic_algorithm(population_size, chromosome_length, num_generations, crossover_rate, mutation_rate):
    population = init_population(population_size, chromosome_length)
    for i in range(num_generations):
        fitness_scores = calculate_fitness(population)
        selected_population = select(population, fitness_scores)
        selected_population = crossover(selected_population, crossover_rate)
        selected_population = mutate(selected_population, mutation_rate)
        population = selected_population
    return max(calculate_fitness(population))

# 调用遗传算法函数
print(genetic_algorithm(100, 10, 100, 0.8, 0.01))

在这个实现中，我们使用了二进制编码来表示染色体的值。每个染色体都是由 0 和 1 组成的一串二进制数。在初始化种群时，我们随机生成了一些二进制数作为染色体的值。在计算适应度时，我们将每个染色体的二进制数转换为十进制数，并将其代入目标函数中计算适应度。在选择，交叉和变异中，我们使用了常见的遗传算法操作。最终，我们返回种群中适应度最高的染色体对应的目标函数的值。