python遗传算法代码

以下是一个简单的Python遗传算法代码的例子，用于优化一个简单的函数：

import random

# 定义适应度函数
def fitness(individual):
    x = individual[0]
    y = individual[1]
    return x**2 + y**2

# 定义交叉函数
def crossover(individual1, individual2):
    crossover_point = random.randint(1, len(individual1) - 1)
    child1 = individual1[:crossover_point] + individual2[crossover_point:]
    child2 = individual2[:crossover_point] + individual1[crossover_point:]
    return child1, child2

# 定义变异函数
def mutate(individual, mutation_probability):
    for i in range(len(individual)):
        if random.random() < mutation_probability:
            individual[i] = random.uniform(-5, 5)
    return individual

# 定义遗传算法函数
def genetic_algorithm(population_size, fitness_function, crossover_function, mutation_function, mutation_probability, num_generations):
    # 初始化种群
    population = []
    for i in range(population_size):
        individual = [random.uniform(-5, 5), random.uniform(-5, 5)]
        population.append(individual)

    # 进化
    for generation in range(num_generations):
        # 计算适应度
        fitness_values = [fitness_function(individual) for individual in population]

        # 选择
        total_fitness = sum(fitness_values)
        selection_probabilities = [fitness_value / total_fitness for fitness_value in fitness_values]
        selected_indices = random.choices(range(population_size), weights=selection_probabilities, k=population_size)
        selected_population = [population[i] for i in selected_indices]

        # 交叉
        children = []
        for i in range(0, population_size, 2):
            child1, child2 = crossover_function(selected_population[i], selected_population[i+1])
            children.append(child1)
            children.append(child2)

        # 变异
        mutated_children = [mutation_function(child, mutation_probability) for child in children]

        # 更新种群
        population = mutated_children

    # 返回最佳个体
    fitness_values = [fitness_function(individual) for individual in population]
    best_index = fitness_values.index(min(fitness_values))
    return population[best_index]

# 测试
best_individual = genetic_algorithm(100, fitness, crossover, mutate, 0.1, 100)
print("最佳个体：", best_individual)
print("最佳适应度：", fitness(best_individual))

在这个例子中，我们尝试寻找一个函数的最小值。我们使用遗传算法来优化函数 $f(x, y) = x^2 + y^2$，其中 $x$ 和 $y$ 的值在 $[-5, 5]$ 范围内。我们使用随机初始化种群，并通过适应度函数计算每个个体的适应度。在每一代，我们选择最适应的个体，并使用交叉和变异操作来生成新个体。在这个例子中，我们使用单点交叉和随机均匀变异。遗传算法运行 100 代，然后返回最适应的个体。