采用遗传算法求函数最优解，完整代码

下面是一个使用Python实现遗传算法求解函数最优解的示例代码。本代码以求解函数 f(x) = x^2 在区间 [0, 31] 上的最小值为例。

import random

# 定义函数
def f(x):
    return x**2

# 遗传算法参数
pop_size = 50 # 种群大小
elite_size = 5 # 精英个体数
mutation_rate = 0.1 # 变异率
generations = 30 # 迭代次数

# 初始化种群
def init_population(pop_size):
    population = []
    for i in range(pop_size):
        individual = random.uniform(0, 31) # 随机生成一个个体
        population.append(individual)
    return population

# 评估适应度
def fitness(individual):
    return f(individual)

# 选择
def selection(population):
    fitnesses = [fitness(individual) for individual in population]
    elite = sorted(range(len(fitnesses)), key=lambda i: fitnesses[i])[:elite_size] # 精英个体索引
    parents = [population[i] for i in elite] # 精英个体作为父母
    return parents

# 交叉
def crossover(parents):
    children = []
    for i in range(len(parents)):
        parent1 = parents[i]
        parent2 = parents[(i+1)%len(parents)]
        child = (parent1 + parent2) / 2 # 均值交叉
        children.append(child)
    return children

# 变异
def mutation(children):
    for i in range(len(children)):
        if random.random() < mutation_rate:
            children[i] = random.uniform(0, 31) # 随机变异
    return children

# 遗传算法主函数
def genetic_algorithm(population):
    for i in range(generations):
        parents = selection(population)
        children = crossover(parents)
        children = mutation(children)
        population = parents + children
    best_individual = min(population, key=fitness)
    return best_individual

# 测试
population = init_population(pop_size)
best_individual = genetic_algorithm(population)
print("最优解：", best_individual)
print("最小值：", f(best_individual))

在上述代码中，我们首先定义了待求解的函数 `f(x)`，然后定义了遗传算法的参数，包括种群大小、精英个体数、变异率和迭代次数。接着，我们实现了遗传算法的核心函数，包括初始化种群、评估适应度、选择、交叉和变异。最后，我们在主函数中调用上述函数，得到最优解和最小值。

需要注意的是，本代码仅为示例，未对遗传算法的性能做出优化。在实际应用中，需要根据具体问题进行参数调整和性能优化。