Hyper heuristic based on great deluge and its variants for exam
  timetabling problem
  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.
