能否提供一个遗传算法在课程调度问题中的具体实现代码示例？

遗传算法是一种模拟自然选择过程的优化算法，适用于解决复杂的优化问题。在课程调度问题中，目标通常是找到一种最优的时间表安排，使得学生可以满足所有必修课和选修课的需求，并尽量避免冲突。这里给出一个简单的Python代码示例，使用DEAP库来实现遗传算法求解课程调度问题：

from deap import base, creator, tools, algorithms

# 定义课程和时间表结构
creator.create("FitnessMax", base.Fitness, weights=(1.0,))
creator.create("Individual", list, fitness=creator.FitnessMax)

def eval_course_conflict(individual):
    # 模拟课程冲突检查函数，实际应用中需要根据具体课程时间表数据
    conflicts = count_conflicts(individual)
    return -conflicts,  # 越少冲突，优先级越高

toolbox = base.Toolbox()
toolbox.register("individual", tools.initRepeat, creator.Individual, range(1, len(courses)+1))
toolbox.register("population", tools.initRepeat, list, toolbox.individual)
toolbox.register("evaluate", eval_course_conflict)
toolbox.register("mate", tools.cxTwoPoint)  # 遗传操作：交叉
toolbox.register("mutate", tools.mutShuffleIndexes, indpb=0.05)  # 遗传操作：突变
toolbox.register("select", tools.selTournament, tournsize=3)  # 选择操作：锦标赛选择

def main():
    pop = toolbox.population(n=50)
    for gen in range(100):  # 迭代次数
        offspring = algorithms.varAnd(pop, toolbox, cxpb=0.9, mutpb=0.1)
        fits = [toolbox.evaluate(i) for i in offspring]
        offspring = [ind for ind, fit in zip(offspring, fits) if fit[0] < 0] + toolbox.select(offspring, len(pop))
        pop[:] = offspring

    best_ind = max(pop, key=lambda ind: ind.fitness.values[0])
    print(f"Best individual (fitness: {best_ind.fitness.values[0]}): {best_ind}")

if __name__ == "__main__":
    main()