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一周5天每天排7节课语数英只排每天前3节体育不排每天前3节 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

# 定义班级和科目信息
grades = ['一年级', '二年级', '三年级', '四年级', '五年级', '六年级']
classes = ['A班', 'B班', 'C班']
subjects = ['语文', '数学', '英语', '体育', '其他科目']

# 定义每个年级每个班级的科目课时量
subject_hours = {
    '一年级': {'A班': {'语文': 5, '数学': 5, '英语': 5, '体育': 2, '其他科目': 1},
             'B班': {'语文': 5, '数学': 5, '英语': 5, '体育': 2, '其他科目': 1},
             'C班': {'语文': 5, '数学': 5, '英语': 5, '体育': 2, '其他科目': 1}},
    '二年级': {'A班': {'语文': 4, '数学': 4, '英语': 4, '体育': 2, '其他科目': 2},
             'B班': {'语文': 4, '数学': 4, '英语': 4, '体育': 2, '其他科目': 2},
             'C班': {'语文': 4, '数学': 4, '英语': 4, '体育': 2, '其他科目': 2}},
    ...
}

# 定义每个教师的课时量
teacher_hours = {
    '教师1': {'语文': 15, '数学': 15, '英语': 15, '体育': 6, '其他科目': 3},
    '教师2': {'语文': 15, '数学': 15, '英语': 15, '体育': 6, '其他科目': 3},
    ...
}

# 定义遗传算法参数
population_size = 50
max_generations = 100
mutation_rate = 0.01

# 初始化种群
def init_population():
    population = []
    for _ in range(population_size):
        chromosome = []
        for grade in grades:
            for class_name in classes:
                for subject in subjects:
                    hours = subject_hours[grade][class_name][subject]
                    chromosome.extend([subject] * hours)
        random.shuffle(chromosome)
        population.append(chromosome)
    return population

# 计算适应度函数
def fitness(chromosome):
    fitness_score = 0
    for i in range(0, len(chromosome), 7):
        day_schedule = chromosome[i:i+7]
        language_hours = day_schedule[:3].count('语文') + day_schedule[:3].count('数学') + day_schedule[:3].count('英语')
        if language_hours == 3:
            fitness_score += 1
    return fitness_score

# 交叉操作
def crossover(parent1, parent2):
    crossover_point = random.randint(1, len(parent1) - 2)
    child1 = parent1[:crossover_point] + parent2[crossover_point:]
    child2 = parent2[:crossover_point] + parent1[crossover_point:]
    return child1, child2

# 变异操作
def mutate(chromosome):
    for i in range(len(chromosome)):
        if random.random() < mutation_rate:
            random_index = random.randint(0, len(chromosome) - 1)
            chromosome[i], chromosome[random_index] = chromosome[random_index], chromosome[i]
    return chromosome

# 选择操作
def selection(population, fitness_scores):
    total_fitness = sum(fitness_scores)
    probabilities = [score / total_fitness for score in fitness_scores]
    selected_indices = random.choices(range(len(population)), probabilities, k=2)
    return population[selected_indices[0]], population[selected_indices[1]]

# 进化过程
def evolve(population):
    for _ in range(max_generations):
        fitness_scores = [fitness(chromosome) for chromosome in population]
        new_population = []
        for _ in range(population_size // 2):
            parent1, parent2 = selection(population, fitness_scores)
            child1, child2 = crossover(parent1, parent2)
            new_population.append(mutate(child1))
            new_population.append(mutate(child2))
        population = new_population
    return population

# 打印最优解
def print_schedule(chromosome):
    for i in range(0, len(chromosome), 7):
        day_schedule = chromosome[i:i+7]
        print('第{}节课：{}'.format(i//7+1, day_schedule))

# 主函数
def main():
    population = init_population()
    population = evolve(population)
    best_chromosome = max(population, key=fitness)
    print_schedule(best_chromosome)

if __name__ == '__main__':
    main()

这个代码使用遗传算法来排课，首先定义了班级和科目信息，以及每个年级每个班级的科目课时量和每个教师的课时量。然后初始化种群，计算适应度函数，实现交叉、变异和选择操作，最后进行进化过程。最优解即为最适应的课程表。最后输出最优解的课程表