基础遗传算法代码

以下是一个基础的遗传算法代码示例，用于解决优化问题：

import random
import numpy as np

# 定义适应度函数，这里以简单的函数 f(x)=x^2 为例
def fitness_func(x):
    return x ** 2

# 定义初始化种群函数
def init_population(pop_size, chrom_length):
    population = np.zeros((pop_size, chrom_length), dtype=int)
    for i in range(pop_size):
        for j in range(chrom_length):
            population[i][j] = random.randint(0, 1)
    return population

# 定义选择操作函数
def selection(population, fitness_values):
    select_prob = fitness_values / np.sum(fitness_values)
    cum_prob = np.cumsum(select_prob)
    new_population = np.zeros_like(population)
    for i in range(len(population)):
        r = random.random()
        for j in range(len(cum_prob)):
            if r <= cum_prob[j]:
                new_population[i] = population[j]
                break
    return new_population

# 定义交叉操作函数
def crossover(parents, pc):
    offspring = np.zeros_like(parents)
    for i in range(0, len(parents), 2):
        if i+1 < len(parents):
            if random.random() < pc:
                crossover_point = random.randint(1, len(parents[i])-1)
                offspring[i][:crossover_point] = parents[i][:crossover_point]
                offspring[i][crossover_point:] = parents[i+1][crossover_point:]
                offspring[i+1][:crossover_point] = parents[i+1][:crossover_point]
                offspring[i+1][crossover_point:] = parents[i][crossover_point:]
            else:
                offspring[i] = parents[i]
                offspring[i+1] = parents[i+1]
        else:
            offspring[i] = parents[i]
    return offspring

# 定义变异操作函数
def mutation(offspring, pm):
    for i in range(len(offspring)):
        for j in range(len(offspring[i])):
            if random.random() < pm:
                offspring[i][j] = 1 if offspring[i][j] == 0 else 0
    return offspring

# 定义主函数
def main():
    # 定义参数
    pop_size = 10
    chrom_length = 8
    max_gen = 100
    pc = 0.7
    pm = 0.001

    # 初始化种群
    population = init_population(pop_size, chrom_length)

    # 开始迭代
    for gen in range(max_gen):
        # 计算适应度值
        fitness_values = np.array([fitness_func(chrom) for chrom in population])
        # 选择操作
        new_population = selection(population, fitness_values)
        # 交叉操作
        new_population = crossover(new_population, pc)
        # 变异操作
        new_population = mutation(new_population, pm)
        # 更新种群
        population = new_population

    # 找到最优解
    best_fitness = -1
    for chrom in population:
        fitness = fitness_func(chrom)
        if fitness > best_fitness:
            best_fitness = fitness
            best_chrom = chrom

    print('最优解为:', best_chrom, '，对应的适应度值为:', best_fitness)

if __name__ == '__main__':
    main()

注意，这里的适应度函数是一个简单的二次函数 $f(x) = x^2$，在实际中需要根据具体问题设计适应度函数。此外，种群的染色体长度、交叉率、变异率等参数也需要根据具体问题进行调整。