请你给我写一个简单的遗传算法的Python代码

当然，下面是一个简单的遗传算法的 Python 代码示例：

import numpy as np

POP_SIZE = 50  # 种群大小
GENE_LENGTH = 10  # 染色体长度
CROSS_RATE = 0.8  # 交叉概率
MUTATION_RATE = 0.01  # 变异概率
N_GENERATIONS = 100  # 迭代次数

# 初始化种群
population = np.random.randint(0, 2, (POP_SIZE, GENE_LENGTH))

# 定义适应度函数
def fitness(population):
    return np.sum(population, axis=1)

# 定义选择操作
def select(population, fitness):
    idx = np.random.choice(np.arange(POP_SIZE), size=POP_SIZE, replace=True, p=fitness/fitness.sum())
    return population[idx]

# 定义交叉操作
def crossover(parents):
    offspring = np.empty((POP_SIZE, GENE_LENGTH))
    for i in range(POP_SIZE):
        p1, p2 = parents[i], parents[np.random.randint(0, POP_SIZE)]
        crossover_point = np.random.randint(0, 2, GENE_LENGTH).astype(np.bool)
        offspring[i] = p1
        offspring[i, crossover_point] = p2[crossover_point]
    return offspring

# 定义变异操作
def mutate(offspring):
    for i in range(POP_SIZE):
        for j in range(GENE_LENGTH):
            if np.random.rand() < MUTATION_RATE:
                offspring[i, j] = 1 - offspring[i, j]
    return offspring

# 主循环
for generation in range(N_GENERATIONS):
    # 计算适应度
    fit = fitness(population)
    
    # 选择操作
    parents = select(population, fit)
    
    # 交叉操作
    offspring = crossover(parents)
    
    # 变异操作
    offspring = mutate(offspring)
    
    # 更新种群
    population = offspring

# 打印最优个体
best_idx = np.argmax(fit)
print("Best individual:", population[best_idx])

这个示例代码实现了一个简单的遗传算法，通过随机生成二进制的染色体，并进行选择、交叉和变异操作，最终找到适应度最高的个体作为最优解。遗传算法的核心思想是通过模拟生物进化的过程，在种群中搜索最优解。