用Python 写一个基于遗传算法的排班代码

好的，以下是基于遗传算法的排班代码示例：

import random

# 定义员工信息和班次信息
employees = ["A", "B", "C", "D", "E", "F", "G", "H"]
shifts = ["早班", "中班", "晚班"]

# 定义遗传算法参数
POPULATION_SIZE = 20
GENERATIONS = 100
MUTATION_RATE = 0.1

# 定义适应度函数，计算并返回适应度值
def fitness(individual):
    score = 0
    for i in range(len(individual)):
        if individual[i] == shifts[0]:
            if employees[i] not in ["B", "E", "G"]:
                score += 1
        elif individual[i] == shifts[1]:
            if employees[i] not in ["A", "C", "D", "F"]:
                score += 1
        else:
            if employees[i] not in ["A", "C", "E", "H"]:
                score += 1
    return score

# 定义交叉函数，返回两个父代的子代
def crossover(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

# 定义变异函数，返回变异后的个体
def mutation(individual):
    mutated_individual = individual[:]
    gene_to_mutate = random.randint(0, len(mutated_individual)-1)
    mutated_individual[gene_to_mutate] = random.choice(shifts)
    return mutated_individual

# 初始化种群
population = []
for i in range(POPULATION_SIZE):
    individual = [random.choice(shifts) for j in range(len(employees))]
    population.append(individual)

# 迭代
for generation in range(GENERATIONS):
    # 计算适应度值
    fitness_scores = []
    for individual in population:
        fitness_scores.append(fitness(individual))

    # 找到适应度值最高的个体
    best_individual_index = fitness_scores.index(max(fitness_scores))
    best_individual = population[best_individual_index]
    print("第{}代最优解：{}，适应度值：{}".format(generation, best_individual, max(fitness_scores)))

    # 选择
    selected_parents = []
    for i in range(2):
        parent1 = population[random.randint(0, POPULATION_SIZE-1)]
        parent2 = population[random.randint(0, POPULATION_SIZE-1)]
        if fitness(parent1) > fitness(parent2):
            selected_parents.append(parent1)
        else:
            selected_parents.append(parent2)

    # 交叉
    child1, child2 = crossover(selected_parents[0], selected_parents[1])

    # 变异
    if random.random() < MUTATION_RATE:
        child1 = mutation(child1)
    if random.random() < MUTATION_RATE:
        child2 = mutation(child2)

    # 更新种群
    population[best_individual_index] = child1
    population[random.randint(0, POPULATION_SIZE-1)] = child2

这是一个简单的排班程序，使用遗传算法来生成最优的员工排班方案。程序中，我们定义了员工信息和班次信息，以及遗传算法的参数。适应度函数根据员工能否胜任某个班次来计算适应度值，交叉和变异函数用来生成新的个体。在迭代过程中，我们选择适应度值最高的两个个体作为父代，通过交叉和变异生成两个子代，并更新种群。最终，程序会输出每一代的最优解和适应度值。