用遗传算法求解下面函数的最大值约为2.118。用python语言编写代码。 f(х,у)=1+xsin(4πx)-ysin(4πy)+sin(6√(x²+y²))/6(√(x²+y²+10^(-15))) ,x,y∈[-1,1]

以下是用遗传算法求解该函数最大值的Python代码：

import math
import random

# 定义函数
def f(x, y):
    return 1 + x * math.sin(4 * math.pi * x) - y * math.sin(4 * math.pi * y) + math.sin(6 * math.sqrt(x**2 + y**2)) / (6 * math.sqrt(x**2 + y**2 + 10**(-15)))

# 定义遗传算法参数
POP_SIZE = 100 # 种群大小
GENERATIONS = 100 # 迭代次数
MUTATION_RATE = 0.01 # 突变率

# 定义种群类
class Population:
    def __init__(self, pop_size):
        self.pop_size = pop_size
        self.population = []
        for i in range(pop_size):
            x = random.uniform(-1, 1)
            y = random.uniform(-1, 1)
            self.population.append((x, y))
    
    def fitness(self):
        fitness_values = []
        for i in range(self.pop_size):
            fitness_values.append(f(self.population[i][0], self.population[i][1]))
        return fitness_values
    
    def select(self, fitness_values):
        max_fitness = max(fitness_values)
        fitness_sum = sum(fitness_values)
        if fitness_sum == 0:
            return self.population[random.randint(0, self.pop_size-1)]
        probabilities = [fitness_values[i] / fitness_sum for i in range(self.pop_size)]
        cum_probs = [sum(probabilities[:i+1]) for i in range(self.pop_size)]
        r = random.uniform(0, 1)
        for i in range(self.pop_size):
            if r <= cum_probs[i]:
                return self.population[i]
    
    def crossover(self, parent1, parent2):
        x1, y1 = parent1
        x2, y2 = parent2
        child1 = (x1, y2)
        child2 = (x2, y1)
        return child1, child2
    
    def mutate(self, child):
        x, y = child
        if random.random() < MUTATION_RATE:
            x = random.uniform(-1, 1)
        if random.random() < MUTATION_RATE:
            y = random.uniform(-1, 1)
        return (x, y)
    
    def next_generation(self):
        fitness_values = self.fitness()
        new_population = []
        for i in range(self.pop_size//2):
            parent1 = self.select(fitness_values)
            parent2 = self.select(fitness_values)
            child1, child2 = self.crossover(parent1, parent2)
            child1 = self.mutate(child1)
            child2 = self.mutate(child2)
            new_population.extend([child1, child2])
        self.population = new_population

# 运行遗传算法
population = Population(POP_SIZE)
for i in range(GENERATIONS):
    print("Generation:", i+1)
    fitness_values = population.fitness()
    max_fitness = max(fitness_values)
    max_index = fitness_values.index(max_fitness)
    print("Max fitness:", max_fitness)
    print("Best solution:", population.population[max_index])
    population.next_generation()

该代码会输出每一代的最大适应度和最佳解，最后输出的最佳解即为所求的函数最大值。