小学课程安排遗传算法 Python 源码：高效优化您的课程表

以下是一个简单的基于遗传算法的小学排课程的 Python 源码示例：

import random

# 遗传算法参数
POPULATION_SIZE = 100  # 种群数量
CROSSOVER_RATE = 0.8  # 交叉概率
MUTATION_RATE = 0.2  # 变异概率
TOURNAMENT_SIZE = 5  # 锦标赛选择的参与者数量
ELITE_SIZE = 1  # 精英数量
GENERATIONS = 100  # 迭代次数

# 学生、课程和教师的数量
NUM_STUDENTS = 30
NUM_COURSES = 5
NUM_TEACHERS = 5

# 课程表类
class Timetable:
    def __init__(self, num_students, num_courses, num_teachers):
        self.num_students = num_students
        self.num_courses = num_courses
        self.num_teachers = num_teachers
        self.timetable = [[[] for _ in range(num_courses)] for _ in range(num_teachers)]
        
    def add_course(self, teacher, course):
        for i in range(self.num_courses):
            if course in self.timetable[teacher][i]:
                return False  # 该教师在该时间段已经有课程
        self.timetable[teacher][i].append(course)
        return True
    
    def remove_course(self, teacher, course):
        self.timetable[teacher][course].remove(course)
        
    def fitness(self):
        # 计算适应度，适应度越高表示课程安排越好
        # 这里可以根据实际需求定义适应度的计算方法
        pass

# 个体类
class Individual:
    def __init__(self, timetable):
        self.timetable = timetable
        self.fitness = 0
    
    def generate(self):
        # 生成随机的课程安排
        for teacher in range(self.timetable.num_teachers):
            for course in range(self.timetable.num_courses):
                if random.random() < 0.5:
                    self.timetable.add_course(teacher, course)
    
    def calculate_fitness(self):
        self.fitness = self.timetable.fitness()
    
    def crossover(self, partner):
        child = Individual(self.timetable)
        for teacher in range(self.timetable.num_teachers):
            for course in range(self.timetable.num_courses):
                if random.random() < CROSSOVER_RATE:
                    child.timetable.add_course(teacher, course)
                else:
                    child.timetable.add_course(partner.timetable[teacher][course])
        return child
    
    def mutate(self):
        for teacher in range(self.timetable.num_teachers):
            for course in range(self.timetable.num_courses):
                if random.random() < MUTATION_RATE:
                    if self.timetable[teacher][course]:
                        self.timetable.remove_course(teacher, course)
                    else:
                        self.timetable.add_course(teacher, course)

# 遗传算法类
class GeneticAlgorithm:
    def __init__(self, num_students, num_courses, num_teachers):
        self.num_students = num_students
        self.num_courses = num_courses
        self.num_teachers = num_teachers
        self.population = []
        
    def initial_population(self):
        for _ in range(POPULATION_SIZE):
            timetable = Timetable(self.num_students, self.num_courses, self.num_teachers)
            individual = Individual(timetable)
            individual.generate()
            self.population.append(individual)
    
    def evolve_population(self):
        new_population = []
        for _ in range(ELITE_SIZE):
            new_population.append(self.get_fittest())
        
        for _ in range(POPULATION_SIZE - ELITE_SIZE):
            parent1 = self.tournament_selection()
            parent2 = self.tournament_selection()
            child = parent1.crossover(parent2)
            child.mutate()
            new_population.append(child)
        
        self.population = new_population
    
    def get_fittest(self):
        fittest = self.population[0]
        for individual in self.population:
            if individual.fitness > fittest.fitness:
                fittest = individual
        return fittest
    
    def tournament_selection(self):
        tournament_population = random.sample(self.population, TOURNAMENT_SIZE)
        return max(tournament_population, key=lambda x: x.fitness)

# 主程序
def main():
    ga = GeneticAlgorithm(NUM_STUDENTS, NUM_COURSES, NUM_TEACHERS)
    ga.initial_population()
    
    for _ in range(GENERATIONS):
        ga.evolve_population()
    
    best_individual = ga.get_fittest()
    print('Best individual fitness:', best_individual.fitness)

if __name__ == '__main__':
    main()

请注意，这只是一个简单的示例代码，可能需要根据具体需求进行修改和完善。在实际应用中，还需要定义适应度函数、课程表的生成和变异方法等。