遗传算法PYthon代码

遗传算法的 Python 代码实现如下：

import random

# 定义适应度函数，此处以求解 x^2 的最大值为例
def fitness_func(x):
    return x ** 2

# 定义遗传算法函数
def genetic_algorithm(population_size, gene_size, crossover_rate, mutation_rate, generations):
    # 初始化种群
    population = [[random.randint(0, 1) for _ in range(gene_size)] for _ in range(population_size)]
    
    for generation in range(generations):
        # 计算适应度
        fitness_scores = [fitness_func(int(''.join(str(gene) for gene in chromosome), 2)) for chromosome in population]
        
        # 选择
        total_fitness = sum(fitness_scores)
        selection_probabilities = [fitness_score / total_fitness for fitness_score in fitness_scores]
        cumulative_probabilities = [sum(selection_probabilities[:i+1]) for i in range(population_size)]
        selected_population = []
        for _ in range(population_size):
            random_number = random.random()
            for i in range(population_size):
                if random_number <= cumulative_probabilities[i]:
                    selected_population.append(population[i])
                    break
                    
        # 交叉
        for i in range(0, population_size, 2):
            if random.random() < crossover_rate:
                crossover_point = random.randint(1, gene_size-1)
                selected_population[i][:crossover_point], selected_population[i+1][:crossover_point] = \
                    selected_population[i+1][:crossover_point], selected_population[i][:crossover_point]
                    
        # 变异
        for i in range(population_size):
            for j in range(gene_size):
                if random.random() < mutation_rate:
                    selected_population[i][j] = 1 - selected_population[i][j]
        
        population = selected_population
    
    # 返回最优解
    best_chromosome = max(population, key=lambda chromosome: fitness_func(int(''.join(str(gene) for gene in chromosome), 2)))
    return int(''.join(str(gene) for gene in best_chromosome), 2)

其中，`population_size` 表示种群大小，`gene_size` 表示基因的长度，`crossover_rate` 表示交叉概率，`mutation_rate` 表示变异概率，`generations` 表示迭代次数。适应度函数 `fitness_func` 可根据具体问题进行定义。