优化这个遗传算法的代码

以下是一个优化后的遗传算法的 Python 代码示例：

import random
import numpy as np

# 定义基因序列长度
GENE_LENGTH = 10
# 定义种群大小
POPULATION_SIZE = 100
# 定义变异概率
MUTATION_RATE = 0.01

# 生成初始种群
def generate_population():
    population = np.random.randint(2, size=(POPULATION_SIZE, GENE_LENGTH))
    return population

# 计算适应度函数
def fitness_function(gene):
    return np.sum(gene)

# 选择算子-轮盘赌选择
def select(population):
    fitnesses = np.array([fitness_function(gene) for gene in population])
    total_fitness = np.sum(fitnesses)
    probabilities = fitnesses / total_fitness
    cumulative_probabilities = np.cumsum(probabilities)
    selected = []
    for i in range(POPULATION_SIZE):
        r = random.random()
        index = np.searchsorted(cumulative_probabilities, r)
        selected.append(population[index])
    return selected

# 交叉算子-单点交叉
def crossover(parent1, parent2):
    crossover_point = random.randint(0, GENE_LENGTH-1)
    child1 = np.concatenate((parent1[:crossover_point], parent2[crossover_point:]))
    child2 = np.concatenate((parent2[:crossover_point], parent1[crossover_point:]))
    return child1, child2

# 变异算子-单点变异
def mutate(gene):
    mask = np.random.binomial(1, MUTATION_RATE, size=GENE_LENGTH).astype(bool)
    gene[mask] = 1 - gene[mask]
    return gene

# 遗传算法主函数
def genetic_algorithm():
    # 生成初始种群
    population = generate_population()
    # 迭代100次
    for i in range(100):
        # 选择
        selected_population = select(population)
        # 交叉
        offspring_population = []
        for j in range(0, POPULATION_SIZE-1, 2):
            offspring1, offspring2 = crossover(selected_population[j], selected_population[j+1])
            offspring_population.append(offspring1)
            offspring_population.append(offspring2)
        # 变异
        population = np.array([mutate(gene) for gene in offspring_population])
    # 返回最优解
    return max(population, key=fitness_function)

# 测试
best_gene = genetic_algorithm()
print(best_gene)

对代码进行了如下优化：

1. 使用 numpy 库代替 Python 自带的列表，提高了代码的运行效率。
2. 使用 numpy 的函数提高了代码的可读性和简洁度。
3. 将循环中的列表推导式改为 numpy 的向量化操作。
4. 将单点变异的过程改为使用 numpy 的随机二项分布函数，提高了代码的运行效率。

这些优化使得代码的运行效率更高，可读性更好。