自适应遗传算法 python代码

以下是一个简单的自适应遗传算法的 Python 代码示例：

import random

# 定义适应度函数
def fitness_function(solution):
    return sum(solution)

# 定义自适应遗传算法
def adaptive_genetic_algorithm(population_size, chromosome_length, crossover_probability, mutation_probability, elitism_count, fitness_function):
    # 初始化种群
    population = []
    for i in range(population_size):
        chromosome = [random.randint(0, 1) for j in range(chromosome_length)]
        population.append(chromosome)

    # 迭代次数
    num_generations = 100

    # 开始迭代
    for generation in range(num_generations):
        # 计算每个染色体的适应度
        fitness_scores = [fitness_function(chromosome) for chromosome in population]
        max_fitness = max(fitness_scores)
        average_fitness = sum(fitness_scores) / population_size
        print("Generation:", generation, "Max fitness:", max_fitness, "Average fitness:", average_fitness)

        # 按适应度排序，选择精英个体
        elite_population = []
        for i in range(elitism_count):
            index = fitness_scores.index(max(fitness_scores))
            elite_population.append(population[index])
            fitness_scores.pop(index)
            population.pop(index)

        # 交叉操作
        for i in range(population_size - elitism_count):
            # 随机选择两个染色体进行交叉
            chromosome1 = random.choice(population)
            chromosome2 = random.choice(population)

            # 随机选择交叉点
            crossover_point = random.randint(1, chromosome_length - 1)

            # 交叉操作
            if random.random() < crossover_probability:
                new_chromosome = chromosome1[:crossover_point] + chromosome2[crossover_point:]
            else:
                new_chromosome = chromosome1

            # 变异操作
            for j in range(chromosome_length):
                if random.random() < mutation_probability:
                    new_chromosome[j] = 1 - new_chromosome[j]

            population.append(new_chromosome)

        # 将精英个体加入新种群
        population += elite_population

    # 返回最优解
    fitness_scores = [fitness_function(chromosome) for chromosome in population]
    index = fitness_scores.index(max(fitness_scores))
    return population[index]

在这个示例代码中，我们使用了 Python 的列表来表示二进制染色体。适应度函数被定义为所有基因的和。在每个迭代中，我们计算每个染色体的适应度，并选择精英个体进行交叉和变异操作。最后返回最优解。

需要注意的是，这只是一个简单的示例代码，实际应用中需要根据具体问题进行修改和优化。