小学分年级分班级不同年级科目和分别指定科目的课时量教师有指定课时量遗传算法排学校总课程表 输出各年级各班一周的课程和相应教师 Python实例代码

以下是一个使用遗传算法排课的Python实例代码：

import random

# 定义学校总课程表类
class Timetable:
    def __init__(self, grades, subjects, teachers, max_hours):
        self.grades = grades  # 年级列表
        self.subjects = subjects  # 科目列表
        self.teachers = teachers  # 教师列表
        self.max_hours = max_hours  # 每门课程的最大课时量

        self.num_grades = len(grades)  # 年级数量
        self.num_subjects = len(subjects)  # 科目数量
        self.num_teachers = len(teachers)  # 教师数量

        self.timetable = [[[[0 for _ in range(max_hours)] for _ in range(self.num_subjects)] for _ in range(self.num_teachers)] for _ in range(self.num_grades)]  # 存储课程表

    # 判断课程表是否满足约束条件
    def is_valid(self):
        # 每门课程的总课时量不能超过最大课时量
        for grade in range(self.num_grades):
            for subject in range(self.num_subjects):
                for teacher in range(self.num_teachers):
                    if sum(self.timetable[grade][teacher][subject]) > self.max_hours[subject]:
                        return False
        return True

    # 生成随机的课程表
    def generate_random_timetable(self):
        for grade in range(self.num_grades):
            for subject in range(self.num_subjects):
                for teacher in range(self.num_teachers):
                    hours = random.randint(0, self.max_hours[subject])  # 随机生成课时量
                    for i in range(hours):
                        self.timetable[grade][teacher][subject][i] = 1

    # 交换两个时间槽的课程
    def swap_courses(self, grade1, teacher1, subject1, hour1, grade2, teacher2, subject2, hour2):
        self.timetable[grade1][teacher1][subject1][hour1], self.timetable[grade2][teacher2][subject2][hour2] = self.timetable[grade2][teacher2][subject2][hour2], self.timetable[grade1][teacher1][subject1][hour1]

    # 计算适应度函数，适应度越高表示课程表的质量越好
    def fitness(self):
        fitness = 0
        # 计算每个教师每门课程的课时量
        teacher_hours = [[0 for _ in range(self.num_subjects)] for _ in range(self.num_teachers)]
        for grade in range(self.num_grades):
            for subject in range(self.num_subjects):
                for teacher in range(self.num_teachers):
                    teacher_hours[teacher][subject] += sum(self.timetable[grade][teacher][subject])

        # 计算教师的适应度
        for teacher in range(self.num_teachers):
            for subject in range(self.num_subjects):
                fitness += abs(teacher_hours[teacher][subject] - self.teachers[teacher][subject])

        return fitness

    # 遗传算法排课
    def genetic_algorithm(self, population_size, num_generations):
        population = []
        for _ in range(population_size):
            timetable_copy = Timetable(self.grades, self.subjects, self.teachers, self.max_hours)
            timetable_copy.generate_random_timetable()
            population.append(timetable_copy)

        for generation in range(num_generations):
            population = sorted(population, key=lambda x: x.fitness())  # 按照适应度排序

            if population[0].fitness() == 0:  # 找到适应度为0的最优解，结束算法
                break

            new_population = []
            elite_size = int(population_size * 0.2)  # 精英个体数量
            new_population.extend(population[:elite_size])  # 保留精英个体

            for _ in range(population_size - elite_size):
                parent1 = random.choice(population[:int(population_size * 0.5)])  # 从前50%的个体中选择父母个体
                parent2 = random.choice(population[:int(population_size * 0.5)])  # 从前50%的个体中选择父母个体

                child = Timetable(self.grades, self.subjects, self.teachers, self.max_hours)
                for grade in range(self.num_grades):
                    for subject in range(self.num_subjects):
                        for teacher in range(self.num_teachers):
                            for hour in range(self.max_hours[subject]):
                                if random.random() < 0.5:  # 以0.5的概率从父母个体中继承课程
                                    child.timetable[grade][teacher][subject][hour] = parent1.timetable[grade][teacher][subject][hour]
                                else:
                                    child.timetable[grade][teacher][subject][hour] = parent2.timetable[grade][teacher][subject][hour]

                # 随机产生变异，以0.1的概率交换两个时间槽的课程
                if random.random() < 0.1:
                    grade1 = random.randint(0, self.num_grades - 1)
                    teacher1 = random.randint(0, self.num_teachers - 1)
                    subject1 = random.randint(0, self.num_subjects - 1)
                    hour1 = random.randint(0, self.max_hours[subject1] - 1)
                    grade2 = random.randint(0, self.num_grades - 1)
                    teacher2 = random.randint(0, self.num_teachers - 1)
                    subject2 = random.randint(0, self.num_subjects - 1)
                    hour2 = random.randint(0, self.max_hours[subject2] - 1)
                    child.swap_courses(grade1, teacher1, subject1, hour1, grade2, teacher2, subject2, hour2)

                new_population.append(child)

            population = new_population

        return population[0]


# 测试代码
grades = ['1年级', '2年级', '3年级']
subjects = ['语文', '数学', '英语']
teachers = [[8, 6, 5], [8, 6, 5], [8, 6, 5]]  # 每个教师每门课的指定课时量
max_hours = [20, 20, 20]  # 每门课的最大课时量

timetable = Timetable(grades, subjects, teachers, max_hours)
best_timetable = timetable.genetic_algorithm(10, 100)

# 打印课程表
for grade in range(timetable.num_grades):
    print(f'{timetable.grades[grade]}:')
    for teacher in range(timetable.num_teachers):
        print(f'教师{teacher + 1}:')
        for subject in range(timetable.num_subjects):
            print(f'{timetable.subjects[subject]}:')
            for hour in range(max_hours[subject]):
                if timetable.timetable[grade][teacher][subject][hour] == 1:
                    print(f'周{hour + 1}')
        print()
    print()

这段代码通过遗传算法来排课，根据每个年级、科目和教师的指定课时量，生成一个合理的课程表。其中，grades、subjects和teachers分别表示年级、科目和教师的列表，max_hours表示每门课程的最大课时量。

在测试代码中，我们定义了3个年级，3个科目和3个教师，每个教师对每门课程的指定课时量分别为8、6、5。然后使用genetic_algorithm方法来进行遗传算法排课，传入种群大小和迭代次数。最后打印出排好的课程表