基于大洪水及其变体的超启发式考试排课算法
Hyper heuristic based on great deluge and its variants for exam timetabling problem
摘要：
目前，高校排课问题每年都有发生，而且解决起来非常困难和耗时。本文描述了基于大规模启发式算法（GD）及其变体的超启发式算法（HH），用于解决来自不同领域的大规模、高约束的排序问题。一般来说，在超启发式框架中，有两个主要阶段：启发式选择和移动接受。本文着重于后一阶段的开发超启发式（HH）框架。本文的主要贡献是将大规模学习（GD）及其变体：弹性学习（FD）、非线性学习（NLGD）、扩展大规模学习（EGD）作为HH中的移动接受方法，并结合强化学习（RL），对这些HH方法在考试基准命题问题上进行了测试，并给出了最佳结果和比较分析。
英文摘要：
Today, University Timetabling problems are occurred annually and they are often hard and time consuming to solve. This paper describes Hyper Heuristics (HH) method based on Great Deluge (GD) and its variants for solving large, highly constrained timetabling problems from different domains. Generally, in hyper heuristic framework, there are two main stages: heuristic selection and move acceptance. This paper emphasizes on the latter stage to develop Hyper Heuristic (HH) framework. The main contribution of this paper is that Great Deluge (GD) and its variants: Flex Deluge(FD), Non-linear(NLGD), Extended Great Deluge(EGD) are used as move acceptance method in HH by combining Reinforcement learning (RL).These HH methods are tested on exam benchmark timetabling problem and best results and comparison analysis are reported.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
