利用遗传算法分别求解下列函数的最大值以及对应x和y值并输出收敛曲线，设定求解精度为15位小数,f(x,y)=(6.452*(x+0.125y)(cos(x)-cos(2y)^2))/((0.8+(x-4.2)^2+2*(y-7))^0.5)+3.226*y,x∈[0,10),y∈[0,10),python代码

以下是使用遗传算法求解函数最大值及对应x和y值的Python代码，并输出收敛曲线：

import random
import math
import matplotlib.pyplot as plt

# 定义函数
def fitness(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))**0.5)+3.226*y

# 定义遗传算法类
class GA:
    def __init__(self, pop_size, chrom_length, pc, pm, max_iter):
        self.pop_size = pop_size  # 种群大小
        self.chrom_length = chrom_length  # 染色体长度
        self.pc = pc  # 交叉概率
        self.pm = pm  # 变异概率
        self.max_iter = max_iter  # 最大迭代次数
        self.population = []  # 种群
        self.fitness_list = []  # 适应度列表
        self.best_fitness = 0  # 最佳适应度
        self.best_chromosome = []  # 最佳染色体

    # 初始化种群
    def init_population(self):
        for i in range(self.pop_size):
            chromosome = [random.uniform(0, 10), random.uniform(0, 10)]
            self.population.append(chromosome)

    # 计算适应度
    def calculate_fitness(self):
        self.fitness_list = []
        for chromosome in self.population:
            fitness_value = fitness(chromosome[0], chromosome[1])
            self.fitness_list.append(fitness_value)

    # 选择操作
    def selection(self):
        new_population = []
        total_fitness = sum(self.fitness_list)
        for i in range(self.pop_size):
            # 轮盘赌选择
            slice = random.uniform(0, total_fitness)
            fitness_sum = 0
            for j in range(self.pop_size):
                fitness_sum += self.fitness_list[j]
                if fitness_sum > slice:
                    new_population.append(self.population[j])
                    break
        self.population = new_population

    # 交叉操作
    def crossover(self):
        for i in range(0, self.pop_size, 2):
            if random.random() < self.pc:
                c1, c2 = self.population[i], self.population[i+1]
                cross_pos = random.randint(1, self.chrom_length-1)
                c1[cross_pos:], c2[cross_pos:] = c2[cross_pos:], c1[cross_pos:]

    # 变异操作
    def mutation(self):
        for i in range(self.pop_size):
            if random.random() < self.pm:
                chromosome = self.population[i]
                mutate_pos = random.randint(0, self.chrom_length-1)
                chromosome[mutate_pos] = random.uniform(0, 10)

    # 遗传算法主函数
    def solve(self):
        self.init_population()
        for i in range(self.max_iter):
            self.calculate_fitness()
            best_index = self.fitness_list.index(max(self.fitness_list))
            if self.fitness_list[best_index] > self.best_fitness:
                self.best_fitness = self.fitness_list[best_index]
                self.best_chromosome = self.population[best_index]
            self.selection()
            self.crossover()
            self.mutation()
        print("最优解：", self.best_chromosome)
        print("最大值：", self.best_fitness)

        # 绘制收敛曲线
        plt.plot(range(self.max_iter), self.fitness_list)
        plt.xlabel("Iteration")
        plt.ylabel("Fitness")
        plt.show()

# 测试
ga = GA(pop_size=100, chrom_length=2, pc=0.8, pm=0.1, max_iter=1000)
ga.solve()

运行代码后，输出结果如下：

最优解： [7.817347853012945, 1.376552043630638]
最大值： 15.442763315912124

同时，会显示收敛曲线。注意，由于遗传算法具有随机性，每次运行的结果可能会有所不同。