遗传算法排课：Python代码示例，解决小学课程安排难题

以下是一个使用遗传算法来排课的简单示例代码，该代码可以用来解决小学课程安排问题，并考虑了班级、老师、科目、课时量等因素。

import random

# 定义班级和老师的信息
classes = ['1A', '1B', '2A', '2B', '3A', '3B', '4A', '4B', '5A', '5B', '6A', '6B']
teachers = ['Teacher1', 'Teacher2', 'Teacher3', 'Teacher4', 'Teacher5', 'Teacher6', 'Teacher7', 'Teacher8', 'Teacher9', 'Teacher10', 'Teacher11']

# 定义不同年级的科目和课时量
subjects = {
    '1': {'Math': 4, 'English': 3, 'Science': 2},
    '2': {'Math': 4, 'English': 3, 'Science': 2},
    '3': {'Math': 5, 'English': 4, 'Science': 2},
    '4': {'Math': 5, 'English': 4, 'Science': 2},
    '5': {'Math': 5, 'English': 4, 'Science': 3},
    '6': {'Math': 5, 'English': 4, 'Science': 3}
}

# 定义每个老师的指定课时量
teacher_hours = {
    'Teacher1': {'Math': 8, 'English': 5, 'Science': 3},
    'Teacher2': {'Math': 10, 'English': 4, 'Science': 2},
    'Teacher3': {'Math': 6, 'English': 6, 'Science': 4},
    'Teacher4': {'Math': 7, 'English': 4, 'Science': 2},
    'Teacher5': {'Math': 9, 'English': 3, 'Science': 3},
    'Teacher6': {'Math': 8, 'English': 5, 'Science': 2},
    'Teacher7': {'Math': 6, 'English': 4, 'Science': 3},
    'Teacher8': {'Math': 7, 'English': 4, 'Science': 3},
    'Teacher9': {'Math': 8, 'English': 4, 'Science': 2},
    'Teacher10': {'Math': 6, 'English': 5, 'Science': 3},
    'Teacher11': {'Math': 7, 'English': 3, 'Science': 2}
}

# 初始化种群
def create_population():
    population = []
    for _ in range(10):
        schedule = []
        for grade in range(1, 7):
            for _ in range(2):
                class_name = random.choice(classes)
                teacher = random.choice(teachers)
                subject = random.choice(list(subjects[str(grade)].keys()))
                schedule.append((class_name, teacher, subject))
        population.append(schedule)
    return population

# 计算适应度函数
def calculate_fitness(schedule):
    fitness = 0
    for grade in range(1, 7):
        grade_schedule = [s for s in schedule if int(s[0][0]) == grade]
        for subject, hours in subjects[str(grade)].items():
            subject_schedule = [s for s in grade_schedule if s[2] == subject]
            if len(subject_schedule) != hours:
                fitness += 1
        for teacher in teachers:
            teacher_schedule = [s for s in grade_schedule if s[1] == teacher]
            teacher_hours_required = teacher_hours[teacher]
            for subject, hours in teacher_hours_required.items():
                teacher_subject_schedule = [s for s in teacher_schedule if s[2] == subject]
                if len(teacher_subject_schedule) != hours:
                    fitness += 1
    return fitness

# 选择操作
def selection(population):
    fitness_scores = [calculate_fitness(schedule) for schedule in population]
    total_fitness = sum(fitness_scores)
    probabilities = [fitness / total_fitness for fitness in fitness_scores]
    selected_population = random.choices(population, probabilities, k=5)
    return selected_population

# 交叉操作
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

# 变异操作
def mutation(schedule):
    mutated_schedule = schedule.copy()
    for _ in range(3):
        index = random.randint(0, len(mutated_schedule) - 1)
        class_name = random.choice(classes)
        teacher = random.choice(teachers)
        subject = random.choice(list(subjects[str(int(schedule[index][0][0]))].keys()))
        mutated_schedule[index] = (class_name, teacher, subject)
    return mutated_schedule

# 遗传算法主程序
def genetic_algorithm():
    population = create_population()
    for _ in range(100):
        selected_population = selection(population)
        new_population = []
        while len(new_population) < 10:
            parent1, parent2 = random.choices(selected_population, k=2)
            child1, child2 = crossover(parent1, parent2)
            mutated_child1 = mutation(child1)
            mutated_child2 = mutation(child2)
            new_population.append(mutated_child1)
            new_population.append(mutated_child2)
        population = new_population
    best_schedule = min(population, key=calculate_fitness)
    print(best_schedule)

# 运行遗传算法
gen