编写python遗传算法

遗传算法是一种仿照生物进化的过程的人工智能算法。下面是一个用 Python 实现的遗传算法的简单例子。

import random

# 初始化种群（随机生成一组解）
def init_population(pop_size, num_bits):
    population = []
    for i in range(pop_size):
        chromosome = ''
        for j in range(num_bits):
            chromosome += str(random.randint(0, 1))
        population.append(chromosome)
    return population

# 评估每个个体的适应度
def evaluate_fitness(population):
    fitness_values = []
    for i in range(len(population)):
        fitness_values.append(population[i].count('1'))
    return fitness_values

# 选择父母（轮盘赌选择）
def select_parents(population, fitness_values):
    parents = []
    total_fitness = sum(fitness_values)
    for i in range(len(population)):
        r = random.uniform(0, total_fitness)
        s = 0
        for j in range(len(population)):
            s += fitness_values[j]
            if s >= r:
                parents.append(population[j])
                break
    return parents

# 交叉（一点交叉）
def crossover(parents, crossover_point):
    children = []
    for i in range(0, len(parents), 2):
        child1 = parents[i][:crossover_point] + parents[i+1][crossover_point:]
        child2 = parents[i+1][:crossover_point] + parents[i][crossover_point:]
        children.append(child1)
        children.append(child2)
    return children

# 变异（随机改变一个基因位置的值）
def mutation(children, mutation_rate):
    for i in range(len(children)):
        for j in range(len(children[i])):
            r = random.uniform(0, 1)
            if r < mutation_rate:
                if children[i][j] == '1':
                    children[i] = children[i][:j] + '0' + children[i][j+1:]
                else:
                    children[i] = children[i][:j] + '1' + children[i][j+1:]
    return children

# 更新种群
def update_population(population, children):
    population = population + children
    return population

# 遗传