人工智能遗传算法f(x,y)=（6.452(x+0.125y)(cos(x)-cos(2y))^2)/(0.8+(x-4.2)^2+2(y-7)^2）+3.226y代码

以下是一个简单的Python代码实现，用遗传算法求解f(x,y)的最大值：

import random
import math

# 目标函数
def f(x, y):
    return (6.452 * (x + 0.125 * y) * (math.cos(x) - math.cos(2 * y)) ** 2) / \
           (0.8 + (x - 4.2) ** 2 + 2 * (y - 7) ** 2) + 3.226 * y

# 遗传算法
def genetic_algorithm():
    # 参数设置
    pop_size = 100 # 种群大小
    chrom_len = 20 # 染色体长度
    p_crossover = 0.8 # 交叉概率
    p_mutation = 0.1 # 变异概率
    max_iter = 100 # 最大迭代次数

    # 初始化种群
    pop = [[random.randint(0, 1) for _ in range(chrom_len)] for _ in range(pop_size)]

    # 迭代寻优
    for i in range(max_iter):
        # 计算适应度
        fits = [f(x, y) for x, y in decode(pop)]

        # 选择操作
        new_pop = []
        for j in range(pop_size):
            parent1 = select(pop, fits)
            parent2 = select(pop, fits)
            child = crossover(parent1, parent2, p_crossover)
            child = mutation(child, p_mutation)
            new_pop.append(child)

        # 更新种群
        pop = new_pop

    # 返回最优解
    return max(decode(pop), key=lambda x: f(x[0], x[1]))

# 解码
def decode(pop):
    return [(x, y) for chrom in pop for x, y in [(chrom[:10], chrom[10:])]]

# 选择操作
def select(pop, fits):
    return pop[roulette(fits)]

# 轮盘赌选择
def roulette(fits):
    total_fit = sum(fits)
    r = random.uniform(0, total_fit)
    cum_fit = 0
    for i in range(len(fits)):
        cum_fit += fits[i]
        if cum_fit > r:
            return i

# 交叉操作
def crossover(parent1, parent2, p_crossover):
    child = parent1[:]
    if random.random() < p_crossover:
        index = random.randint(1, len(parent1) - 1)
        child = parent1[:index] + parent2[index:]
    return child

# 变异操作
def mutation(chrom, p_mutation):
    for i in range(len(chrom)):
        if random.random() < p_mutation:
            chrom[i] = 1 - chrom[i]
    return chrom

# 测试
print(genetic_algorithm())

上述代码中，首先定义了目标函数f(x,y)，然后实现了遗传算法的主要操作，包括初始化种群、计算适应度、选择操作、交叉操作、变异操作等。最后通过调用genetic_algorithm()函数来求解目标函数f(x,y)的最大值。注意，由于目标函数是非凸函数，因此求解结果受初始种群和参数设置的影响较大，可能会得到不同的结果。