# 定义基本数据结构

teachers = ["张老师", "李老师", "王老师"]

classes = ["一年级A班", "二年级B班", "三年级C班"]

rooms = ["教室1", "教室2", "教室3"]

# 创建课程表模板

def create_schedule():

schedule = {teacher: {cls: None for cls in classes} for teacher in teachers}

return schedule

# 示例：打印初始课程表

schedule = create_schedule()

print("初始课程表：", schedule)

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import random

# 遗传算法核心逻辑

def genetic_algorithm(schedule, population_size=100, generations=500):

population = [create_random_schedule() for _ in range(population_size)]

best_solution = None

best_fitness = float('inf')

for gen in range(generations):

fitness_scores = [(fitness(s), s) for s in population]

population = [s for _, s in sorted(fitness_scores, key=lambda x: x[0])]

if fitness_scores[0][0] < best_fitness:

best_fitness = fitness_scores[0][0]

best_solution = population[0]

# 进行交叉和变异操作

new_population = []

for i in range(population_size // 2):

parent1, parent2 = random.choices(population, k=2)

child = crossover(parent1, parent2)

mutate(child)

new_population.extend([child])

population = new_population

return best_solution

# 健康度函数（评估课程表合理性）

def fitness(schedule):

conflicts = 0

for teacher in schedule:

for cls in schedule[teacher]:

if schedule[teacher][cls] is not None and not validate(teacher, cls, schedule[teacher][cls]):

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conflicts += 1

return conflicts

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