掌握遗传算法，学会利用遗传解决函数优化问题。 二 实验内容 用遗传算法求带根号函数的最大值。最大值约为2.118

好的，现在我们有了问题的具体描述。下面是一个使用遗传算法求解的示例代码，您可以根据需要进行修改：

import random
import math

# 定义目标函数，这里使用示例函数 f(x) = x * sin(10 * pi * x) + 2.0
def target_func(x):
    return x * math.sqrt(10 - x) + 2.0

# 定义遗传算法的参数
POPULATION_SIZE = 100  # 种群大小
CROSSOVER_RATE = 0.8  # 交叉概率
MUTATION_RATE = 0.05  # 变异概率
GENERATION_COUNT = 500  # 迭代次数

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

# 计算适应度
def fitness(individual):
    return target_func(individual)

# 进行选择
def selection(population):
    fitness_values = [fitness(individual) for individual in population]
    total_fitness = sum(fitness_values)
    probabilities = [fitness_value / total_fitness for fitness_value in fitness_values]
    selected_indexes = random.choices(range(POPULATION_SIZE), weights=probabilities, k=POPULATION_SIZE)
    return [population[index] for index in selected_indexes]

# 进行交叉
def crossover(parent1, parent2):
    if random.random() < CROSSOVER_RATE:
        crossover_point = random.randint(0, 31)
        child1 = parent1[:crossover_point] + parent2[crossover_point:]
        child2 = parent2[:crossover_point] + parent1[crossover_point:]
        return child1, child2
    else:
        return parent1, parent2

# 进行变异
def mutation(child):
    mutated_child = []
    for bit in child:
        if random.random() < MUTATION_RATE:
            mutated_bit = random.uniform(0, 10)
        else:
            mutated_bit = bit
        mutated_child.append(mutated_bit)
    return mutated_child

# 进行遗传算法求解
def genetic_algorithm():
    population = init_population()
    for generation in range(GENERATION_COUNT):
        sorted_population = sorted(population, key=fitness, reverse=True)
        best_individual = sorted_population[0]
        print(f"Generation {generation}, Best individual: {best_individual}, Fitness: {fitness(best_individual)}")
        selected_population = selection(population)
        next_generation = []
        for i in range(0, POPULATION_SIZE, 2):
            parent1 = selected_population[i]
            parent2 = selected_population[i+1]
            child1, child2 = crossover(parent1, parent2)
            next_generation.append(mutation(child1))
            next_generation.append(mutation(child2))
        population = next_generation

# 运行遗传算法
genetic_algorithm()

这里的示例函数为 $f(x) = x \sqrt{10 - x} + 2.0$，您可以将目标函数替换为您的目标函数。需要注意的是，不同的函数可能需要不同的参数设置和变异操作，您需要根据具体情况进行修改。