遗传算法python实例

很高兴为您提供遗传算法在Python中的示例代码。遗传算法是一类优化算法，可用于解决许多实际问题。以下是一个简单的遗传算法Python示例：

import random

# 创建种群
def create_population(pop_size, num_genes):
    population = []
    for i in range(pop_size):
        individual = []
        for j in range(num_genes):
            individual.append(random.randint(0, 1))
        population.append(individual)
    return population

# 计算适应度
def fitness(individual):
    # 这里编写适应度函数的代码
    score = 0
    for i in individual:
        score += i
    return score

# 选择
def selection(population):
    fitnesses = [fitness(individual) for individual in population]
    sum_fitnesses = sum(fitnesses)
    probabilities = [fitness/sum_fitnesses for fitness in fitnesses]
    selected_indices = []
    for i in range(len(population)):
        selected = False
        while not selected:
            r = random.random()
            for j in range(len(probabilities)):
                if r <= probabilities[j]:
                    selected_indices.append(j)
                    selected = True
                    break
    return [population[index] for index in selected_indices]

# 交叉
def crossover(individual1, individual2):
    crossover_point = random.randint(1, len(individual1) - 1)
    child1 = individual1[:crossover_point] + individual2[crossover_point:]
    child2 = individual2[:crossover_point] + individual1[crossover_point:]
    return [child1, child2]

# 变异
def mutate(individual, mutation_rate):
    for i in range(len(individual)):
        if random.random() < mutation_rate:
            individual[i] = 1 - individual[i]
    return individual

# 主函数
def evolve(pop_size, num_genes, num_iterations, mutation_rate):
    population = create_population(pop_size, num_genes)
    for i in range(num_iterations):
        population = selection(population)
        new_population = []
        for j in range(int(pop_size/2)):
            individual1 = population[random.randint(0, len(population) - 1)]
            individual2 = population[random.randint(0, len(population) - 1)]
            children = crossover(individual1, individual2)
            new_population.extend(children)
        for individual in new_population:
            mutate(individual, mutation_rate)
        population = new_population
    return max(population, key=fitness)

# 示例：
num_genes = 20
pop_size = 100
num_iterations = 1000
mutation_rate = 0.01

print(evolve(pop_size, num_genes, num_iterations, mutation_rate))

请注意，此代码仅用于说明目的。要根据自己的数据和问题进行修改和优化。另外，您可能需要使用其他库来处理您的数据和问题。如果您需要更多帮助，请随时问我。