使用遗传算法优化小学课程表排课 - Python 实例代码

"使用遗传算法优化小学课程表排课 - Python 实例代码"\n\n本文提供了一个使用遗传算法优化小学课程表排课的 Python 实例代码，该代码考虑了不同年级、班级、科目、课时量和教师课时量，并使用遗传算法生成最佳的课程表。\n\n\npython\nimport random\n\n# 定义班级和科目信息\ngrades = ['一年级', '二年级', '三年级', '四年级', '五年级', '六年级']\nclasses = ['A班', 'B班', 'C班']\nsubjects = ['语文', '数学', '英语', '体育', '其他科目']\n\n# 定义每个年级每个班级的科目课时量\nsubject_hours = {\n '一年级': {'A班': {'语文': 5, '数学': 5, '英语': 5, '体育': 2, '其他科目': 1},\n 'B班': {'语文': 5, '数学': 5, '英语': 5, '体育': 2, '其他科目': 1},\n 'C班': {'语文': 5, '数学': 5, '英语': 5, '体育': 2, '其他科目': 1}},\n '二年级': {'A班': {'语文': 4, '数学': 4, '英语': 4, '体育': 2, '其他科目': 2},\n 'B班': {'语文': 4, '数学': 4, '英语': 4, '体育': 2, '其他科目': 2},\n 'C班': {'语文': 4, '数学': 4, '英语': 4, '体育': 2, '其他科目': 2}},\n ...\n}\n\n# 定义每个教师的课时量\nteacher_hours = {\n '教师1': {'语文': 15, '数学': 15, '英语': 15, '体育': 6, '其他科目': 3},\n '教师2': {'语文': 15, '数学': 15, '英语': 15, '体育': 6, '其他科目': 3},\n ...\n}\n\n# 定义遗传算法参数\npopulation_size = 50\nmax_generations = 100\nmutation_rate = 0.01\n\n# 初始化种群\ndef init_population():\n population = []\n for _ in range(population_size):\n chromosome = []\n for grade in grades:\n for class_name in classes:\n for subject in subjects:\n hours = subject_hours[grade][class_name][subject]\n chromosome.extend([subject] * hours)\n random.shuffle(chromosome)\n population.append(chromosome)\n return population\n\n# 计算适应度函数\ndef fitness(chromosome):\n fitness_score = 0\n for i in range(0, len(chromosome), 7):\n day_schedule = chromosome[i:i+7]\n language_hours = day_schedule[:3].count('语文') + day_schedule[:3].count('数学') + day_schedule[:3].count('英语')\n if language_hours == 3:\n fitness_score += 1\n return fitness_score\n\n# 交叉操作\ndef crossover(parent1, parent2):\n crossover_point = random.randint(1, len(parent1) - 2)\n child1 = parent1[:crossover_point] + parent2[crossover_point:]\n child2 = parent2[:crossover_point] + parent1[crossover_point:]\n return child1, child2\n\n# 变异操作\ndef mutate(chromosome):\n for i in range(len(chromosome)):\n if random.random() < mutation_rate:\n random_index = random.randint(0, len(chromosome) - 1)\n chromosome[i], chromosome[random_index] = chromosome[random_index], chromosome[i]\n return chromosome\n\n# 选择操作\ndef selection(population, fitness_scores):\n total_fitness = sum(fitness_scores)\n probabilities = [score / total_fitness for score in fitness_scores]\n selected_indices = random.choices(range(len(population)), probabilities, k=2)\n return population[selected_indices[0]], population[selected_indices[1]]\n\n# 进化过程\ndef evolve(population):\n for _ in range(max_generations):\n fitness_scores = [fitness(chromosome) for chromosome in population]\n new_population = []\n for _ in range(population_size // 2):\n parent1, parent2 = selection(population, fitness_scores)\n child1, child2 = crossover(parent1, parent2)\n new_population.append(mutate(child1))\n new_population.append(mutate(child2))\n population = new_population\n return population\n\n# 打印最优解\ndef print_schedule(chromosome):\n for i in range(0, len(chromosome), 7):\n day_schedule = chromosome[i:i+7]\n print('第{}节课：{}'.format(i//7+1, day_schedule))\n\n# 主函数\ndef main():\n population = init_population()\n population = evolve(population)\n best_chromosome = max(population, key=fitness)\n print_schedule(best_chromosome)\n\nif __name__ == '__main__':\n main()\n\n\n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。\n\n