python实现自适应参数的遗传算法，将步骤用函数实现，给出实现代码

以下是一个将遗传算法步骤封装成函数的 Python 实现，同时使用了自适应参数的方法：

import random
import math

# 定义适应度函数
def fitness(x):
    return x * math.sin(10 * math.pi * x) + 2.0

# 定义选择操作
def selection(population, fitness_list):
    pop_size = len(population)
    sum_fitness = sum(fitness_list)
    probability = [fitness_list[j] / sum_fitness for j in range(pop_size)]
    cumulative_probability = [sum(probability[:k+1]) for k in range(pop_size)]
    new_population = []
    for j in range(pop_size):
        r = random.random()
        for k in range(pop_size):
            if r <= cumulative_probability[k]:
                new_population.append(population[k])
                break
    return new_population

# 定义交叉操作
def crossover(population, pc):
    chrom_len = len(population[0])
    for j in range(0, len(population), 2):
        if random.random() < pc:
            cpoint = random.randint(1, chrom_len-1)
            temp1 = population[j][:cpoint] + population[j+1][cpoint:]
            temp2 = population[j+1][:cpoint] + population[j][cpoint:]
            population[j], population[j+1] = temp1, temp2
    return population

# 定义变异操作
def mutation(population, pm):
    chrom_len = len(population[0])
    for j in range(len(population)):
        if random.random() < pm:
            mpoint = random.randint(0, chrom_len-1)
            population[j][mpoint] = 1 - population[j][mpoint]
    return population

# 定义遗传算法函数
def ga(pop_size=50, chrom_len=20, pc=0.8, pm=0.01, max_iter=100):
    # 初始化
    population = [[random.randint(0, 1) for _ in range(chrom_len)] for _ in range(pop_size)]
    fitness_list = [fitness(int(''.join(map(str, chromosome)), 2)) for chromosome in population]
    best_fitness = -9999
    best_chromosome = []
    # 进化
    for i in range(max_iter):
        # 选择
        new_population = selection(population, fitness_list)
        # 交叉
        population = crossover(new_population, pc)
        # 变异
        population = mutation(population, pm)
        # 计算适应度
        fitness_list = [fitness(int(''.join(map(str, chromosome)), 2)) for chromosome in population]
        # 更新参数
        if max(fitness_list) > best_fitness:
            best_fitness = max(fitness_list)
            best_chromosome = population[fitness_list.index(best_fitness)]
            pc = min(pc + 0.1, 0.95)
            pm = max(pm - 0.01, 0.01)
        else:
            pc = max(pc - 0.1, 0.2)
            pm = min(pm + 0.01, 0.5)
    return best_fitness, best_chromosome

# 测试
print(ga())

在这个实现中，我们将选择、交叉和变异操作分别封装成了三个函数 `selection`、`crossover` 和 `mutation`。在 `ga` 函数中，我们初始化了一些参数，并随机生成了一个初始种群。然后，我们进行 `max_iter` 次迭代，每次迭代分别进行选择、交叉和变异操作，并计算每个个体的适应度。在每次迭代中，我们还会更新交叉和变异概率 `pc` 和 `pm`，以使算法更加自适应。最后，算法返回最优个体的适应度和染色体。

需要注意的是，这个实现中的自适应参数方法只是一种简单的示例，实际应用中可能需要更复杂的方法来确定参数的变化。