用遗传算法求解下列函数的最大值，设定求解精度到15位小数 f(x, y)= (0.432(X+0.125y)(cos(x)-cos(2y))3+3.226v)/(0.8+(x-4.2)3+2(y-7)') x E[0,10),y e[0,10) 用python实现

以下是使用遗传算法求解函数最大值的 Python 代码：

import random

# 定义函数
def f(x, y):
    return (0.432*(x+0.125*y)*(cos(x)-cos(2*y))**3+3.226)/(0.8+(x-4.2)**3+2*(y-7)**2)

# 定义遗传算法参数
POP_SIZE = 100 # 种群大小
GENERATION = 1000 # 迭代次数
MUTATION_RATE = 0.1 # 变异率
CROSSOVER_RATE = 0.8 # 交叉率
ELITISM = True # 是否保留最优个体

# 定义个体
class Individual:
    def __init__(self):
        self.x = random.uniform(0, 10)
        self.y = random.uniform(0, 10)
        self.fitness = 0

    def evaluate(self):
        self.fitness = f(self.x, self.y)

# 定义种群
class Population:
    def __init__(self, size):
        self.size = size
        self.individuals = [Individual() for i in range(size)]

    def evaluate(self):
        for individual in self.individuals:
            individual.evaluate()

    def select(self):
        return random.choice(self.individuals)

    def crossover(self, parent1, parent2):
        child1 = Individual()
        child2 = Individual()
        child1.x = parent1.x
        child1.y = parent2.y
        child2.x = parent2.x
        child2.y = parent1.y
        return child1, child2

    def mutate(self):
        for individual in self.individuals:
            if random.random() < MUTATION_RATE:
                individual.x = random.uniform(0, 10)
                individual.y = random.uniform(0, 10)

    def evolve(self):
        # 评估种群
        self.evaluate()

        # 选择父代
        parent1 = self.select()
        parent2 = self.select()

        # 交叉产生子代
        if random.random() < CROSSOVER_RATE:
            child1, child2 = self.crossover(parent1, parent2)
            self.individuals.append(child1)
            self.individuals.append(child2)

        # 变异
        self.mutate()

        # 选择下一代
        self.individuals = sorted(self.individuals, key=lambda x: x.fitness, reverse=True)[:self.size - int(ELITISM)]

        # 保留最优个体
        if ELITISM:
            self.individuals.append(max(self.individuals, key=lambda x: x.fitness))

# 迭代
pop = Population(POP_SIZE)
for i in range(GENERATION):
    pop.evolve()

# 输出结果
print(max(pop.individuals, key=lambda x: x.fitness).fitness)

注意，由于函数的精度要求比较高，可能需要调整遗传算法的参数以获得更好的结果。