用Python 写一个基于遗传算法的排班代码

好的，我可以为您提供一个基于遗传算法的排班代码的示例。在此之前，需要明确排班的目标和限制条件，例如员工数量、工作时长、休息时间、工作日数等。

以下是一个简单的排班算法实现，使用 Python 语言编写：

import random

# 员工数量
num_employees = 10

# 工作日数
num_days = 7

# 每日工作时长
work_hours = 8

# 员工列表
employees = list(range(num_employees))

# 定义基因
def create_schedule():
    schedule = []
    for i in range(num_days):
        day_schedule = []
        for j in range(num_employees):
            day_schedule.append(random.randint(0, work_hours))
        schedule.append(day_schedule)
    return schedule

# 适应度函数
def fitness(schedule):
    total_hours = 0
    for i in range(num_days):
        for j in range(num_employees):
            total_hours += schedule[i][j]
    return total_hours

# 选择函数
def selection(population):
    fitness_scores = []
    for individual in population:
        fitness_scores.append((individual, fitness(individual)))
    fitness_scores.sort(key=lambda x: x[1])
    return fitness_scores[:int(len(population)*0.5)]

# 交叉函数
def crossover(individual1, individual2):
    child1 = []
    child2 = []
    for i in range(num_days):
        if i % 2 == 0:
            child1.append(individual1[i])
            child2.append(individual2[i])
        else:
            child1.append(individual2[i])
            child2.append(individual1[i])
    return child1, child2

# 变异函数
def mutation(individual):
    for i in range(num_days):
        for j in range(num_employees):
            if random.random() < 0.1:
                individual[i][j] = random.randint(0, work_hours)
    return individual

# 遗传算法主函数
def genetic_algorithm():
    population_size = 100
    population = []
    for i in range(population_size):
        population.append(create_schedule())

    for i in range(1000):
        new_population = []
        elites = selection(population)
        for j in range(population_size - len(elites)):
            parent1 = random.choice(elites)[0]
            parent2 = random.choice(elites)[0]
            child1, child2 = crossover(parent1, parent2)
            child1 = mutation(child1)
            child2 = mutation(child2)
            new_population.append(child1)
            new_population.append(child2)
        new_population += [elite[0] for elite in elites]
        population = new_population

    best_individual = max(population, key=lambda x: fitness(x))
    return best_individual

# 执行遗传算法
best_schedule = genetic_algorithm()

# 输出最佳排班
for i in range(num_days):
    print("Day {}: {}".format(i+1, best_schedule[i]))

这个示例代码仅供参考，具体实现还需要根据实际情况进行调整和优化。