给我用python遗传算法求解有一个函数： f(x)=x+10sin5x+7cos4x 求其在区间[-10,10]之间的最大值。完整代码

在Python中，我们可以使用遗传算法（Genetic Algorithm，GA）来寻找给定函数的最大值。以下是一个简单的例子，使用deap库（Distributed Evolutionary Algorithms in Python）来实现遗传算法。首先，确保你已经安装了deap库，如果没有，可以通过pip安装：

pip install deap

下面是完整的遗传算法代码，用于求解 `f(x)` 的最大值：

import random
from math import sin, cos
from deap import base, creator, tools

# 定义函数 f(x)
def fitness_function(x):
    return -abs(x + 10*sin(5*x) + 7*cos(4*x))

# 初始化问题参数
creator.create("FitnessMax", base.Fitness, weights=(1.0,))
creator.create("Individual", list, fitness=creator.FitnessMax)

toolbox = base.Toolbox()
toolbox.register("attr_float", random.uniform, -10.0, 10.0)
toolbox.register("individual", tools.initRepeat, creator.Individual, toolbox.attr_float, n=1)
toolbox.register("population", tools.initRepeat, list, toolbox.individual)

# 设置交叉和变异操作
toolbox.register("mate", tools.cxTwoPoint)
toolbox.register("mutate", tools.mutGaussian, mu=0, sigma=0.1, indpb=0.05)

# 选择操作
toolbox.register("select", tools.selTournament, tournsize=3)

# 训练代数
POP_SIZE = 50
MAX_GENERATIONS = 100

def main():
    pop = toolbox.population(n=POP_SIZE)
    hof = tools.HallOfFame(1)
    
    for gen in range(MAX_GENERATIONS):
        # 评估个体
        fits = [fitness_function(individual) for individual in pop]
        
        # 选择
        offspring = toolbox.select(pop, len(pop))
        
        # 交叉和变异
        offspring = toolbox.mate(offspring, toolbox.mate)
        offspring = [toolbox.clone(ind) for ind in offspring]
        for child1, child2 in zip(offspring[::2], offspring[1::2]):
            if random.random() < 0.5:
                toolbox.mutate(child1)
            else:
                toolbox.mutate(child2)
        
        # 适应度排序
        fits2 = [fitness_function(ind) for ind in offspring]
        for fit, ind in zip(fits2, offspring):
            ind.fitness.values = fit
            
        # 更新种群
        pop[:] = offspring
        
        # 保存最佳解
        hof.update(pop)

    print("Best individual found is: ", hof[0])
    print("Fitness value of the best individual is: ", hof[0].fitness.values[0])

if __name__ == "__main__":
    main()

这段代码创建了一个种群，并通过迭代来进行交叉、变异和选择操作。每一代结束后，它会保留最好的解作为下一代的开始。运行完成后，`hof[0]` 将包含在 [-10, 10] 区间内找到的最大值附近的解。