用遗传算法求xcosx+2的最大值的Python代码是什么

遗传算法是一种模拟自然选择过程的优化搜索算法，常用于解决复杂的全局优化问题。这里是一个简单的Python代码示例，使用DEAP库（Distributed Evolutionary Algorithms in Python）实现遗传算法来寻找`xcos(x)+2`函数的最大值。请注意，这只是一个基础的框架，并非最优解，因为实际应用可能需要更精细的调整和更多的迭代。

import numpy as np
from deap import base, creator, tools

# 定义问题和适应体
creator.create("FitnessMax", base.Fitness, weights=(1.0,))
creator.create("Individual", list, fitness=creator.FitnessMax)

def eval_fitness(individual):
    x = individual[0]
    return -np.cos(x) + 2, 

toolbox = base.Toolbox()

# 初始化随机个体
toolbox.register("attr_float", lambda: np.random.uniform(-5, 5))
toolbox.register("individual", tools.initRepeat, creator.Individual, toolbox.attr_float, n=1)
toolbox.register("population", tools.initRepeat, list, toolbox.individual)

# 选择、交叉和变异操作
toolbox.register("mate", tools.cxTwoPoint)
toolbox.register("mutate", tools.mutGaussian, mu=0, sigma=1, indpb=0.1)
toolbox.register("select", tools.selTournament, tournsize=3)

# 主循环
ngen = 1000  # 迭代次数
pop = toolbox.population(n=50)
hof = tools.HallOfFame(1)  # 保存最佳解

for gen in range(ngen):
    # 评估当前种群
    fitnesses = list(map(eval_fitness, pop))
    for ind, fit in zip(pop, fitnesses):
        ind.fitness.values = fit
    
    # 选择、交叉和变异
    offspring = [toolbox.clone(ind) for ind in toolbox.select(pop, len(pop))]
    for child1, child2 in zip(offspring[::2], offspring[1::2]):
        if np.random.rand() < 0.5:
            toolbox.mate(child1, child2)
            del child1.fitness.values
            del child2.fitness.values

    for mutant in offspring:
        if np.random.rand() < 0.2:
            toolbox.mutate(mutant)
            del mutant.fitness.values

    # 把新个体加入到种群中
    invalid_ind = [ind for ind in offspring if not ind.fitness.valid]
    fitnesses = map(eval_fitness, invalid_ind)
    for ind, fit in zip(invalid_ind, fitnesses):
        ind.fitness.values = fit

    pop[:] = offspring

    # 保存最佳解
    hof.update(pop)

best_solution = hof[0]
max_value = best_solution.fitness.values[0]

print(f"Best solution found: {best_solution[0]}, Max value of f(x): {max_value}")