python遗传算法_【机器学习】遗传算法(Genetic Algorithm)的Python实现

遗传算法是一种模拟自然进化过程的优化算法，主要用于解决复杂的优化问题。在遗传算法中，通过对个体（解）进行基因操作（交叉、变异等），不断地产生新的个体，并通过选择机制，筛选出适应度高的个体，从而逐步优化得到最优解。

下面是一个简单的遗传算法的Python实现代码：

import random

# 定义适应度函数
def fitness(individual):
    # 适应度函数为 x^2 的形式，其中 x 为个体的染色体长度
    return sum([gene**2 for gene in individual])

# 初始化种群
def init_population(pop_size, gene_size):
    population = []
    for i in range(pop_size):
        individual = [random.randint(0, 1) for j in range(gene_size)]
        population.append(individual)
    return population

# 选择操作
def selection(population):
    # 轮盘赌选择
    fitness_values = [fitness(individual) for individual in population]
    total_fitness = sum(fitness_values)
    probabilities = [fitness/total_fitness for fitness in fitness_values]
    selected_population = []
    for i in range(len(population)):
        selected_individual = None
        while selected_individual is None:
            for j in range(len(population)):
                if random.random() < probabilities[j]:
                    selected_individual = population[j]
                    break
        selected_population.append(selected_individual)
    return selected_population

# 交叉操作
def crossover(parent1, parent2, crossover_rate):
    # 一点交叉
    if random.random() > crossover_rate:
        return parent1, parent2
    crossover_point = random.randint(1, len(parent1)-1)
    child1 = parent1[:crossover_point] + parent2[crossover_point:]
    child2 = parent2[:crossover_point] + parent1[crossover_point:]
    return child1, child2

# 变异操作
def mutation(individual, mutation_rate):
    # 每个基因以 mutation_rate 的概率发生变异
    for i in range(len(individual)):
        if random.random() < mutation_rate:
            individual[i] = 1 - individual[i]
    return individual

# 遗传算法
def genetic_algorithm(pop_size, gene_size, max_generation, crossover_rate, mutation_rate):
    population = init_population(pop_size, gene_size)
    for i in range(max_generation):
        population = selection(population)
        new_population = []
        while len(new_population) < pop_size:
            parent1, parent2 = random.sample(population, 2)
            child1, child2 = crossover(parent1, parent2, crossover_rate)
            child1 = mutation(child1, mutation_rate)
            child2 = mutation(child2, mutation_rate)
            new_population.append(child1)
            new_population.append(child2)
        population = new_population
    best_individual = min(population, key=lambda individual: fitness(individual))
    return best_individual

# 测试
best_individual = genetic_algorithm(pop_size=100, gene_size=10, max_generation=1000, crossover_rate=0.8, mutation_rate=0.1)
print(best_individual, fitness(best_individual))

在上面的代码中，定义了适应度函数、初始化种群、选择、交叉、变异等操作，并通过遗传算法不断迭代，最终得到最优解。在测试中，我们设定种群大小为100，染色体长度为10，最大迭代次数为1000，交叉率为0.8，变异率为0.1，得到的最优解为[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]，适应度函数的值为0。