遗传算法python代码

以下是一个简单的遗传算法的 Python 代码示例： ```python import random # 定义一个适应度函数，本例中为函数 f(x) = x^2 def fitness_func(x): return x ** 2 # 初始化种群 def init_population(population_size, gene_size): population = [] for i in range(population_size): individual = [] for j in range(gene_size): individual.append(random.randint(0, 1)) population.append(individual) return population # 获取每个个体的适应度值 def get_fitness(population): fitness = [] for individual in population: x = int(''.join(map(str, individual)), 2) fitness.append(fitness_func(x)) return fitness # 选择操作 def selection(population, fitness): total_fitness = sum(fitness) probability = [f / total_fitness for f in fitness] probability_sum = [sum(probability[:i+1]) for i in range(len(probability))] new_population = [] for i in range(len(population)): r = random.random() for j in range(len(probability_sum)): if r < probability_sum[j]: new_population.append(population[j]) break return new_population # 交叉操作 def crossover(population): new_population = [] for i in range(0, len(population), 2): individual1 = population[i] individual2 = population[i+1] crossover_point = random.randint(1, len(individual1) - 1) new_individual1 = individual1[:crossover_point] + individual2[crossover_point:] new_individual2 = individual2[:crossover_point] + individual1[crossover_point:] new_population.append(new_individual1) new_population.append(new_individual2) return new_population # 变异操作 def mutation(population, mutation_rate): for i in range(len(population)): for j in range(len(population[i])): if random.random() < mutation_rate: population[i][j] = 1 - population[i][j] return population # 遗传算法主函数 def genetic_algorithm(population_size, gene_size, mutation_rate, max_generation): # 初始化种群 population = init_population(population_size, gene_size) # 迭代 for i in range(max_generation): # 获取每个个体的适应度值 fitness = get_fitness(population) # 选择操作 population = selection(population, fitness) # 交叉操作 population = crossover(population) # 变异操作 population = mutation(population, mutation_rate) # 返回最终的种群和适应度值 fitness = get_fitness(population) return population, fitness ``` 在上面的代码中，我们定义了一个适应度函数 `fitness_func`，并且实现了初始化种群、获取每个个体的适应度值、选择操作、交叉操作和变异操作等函数。最后，我们将这些函数组合起来，实现了一个遗传算法的主函数 `genetic_algorithm`，它接受种群大小、基因长度、变异率和最大迭代次数等参数，返回最终的种群和适应度值。