A Reinforcement Learning - Great-Deluge Hyper-Heuristic for Examination Timetabling

Citation

Ozcan E, Misir M, Ochoa G & Burke E (2010) A Reinforcement Learning - Great-Deluge Hyper-Heuristic for Examination Timetabling. International Journal of Applied Metaheuristic Computing, 1 (1), pp. 39-59. https://doi.org/10.4018/jamc.2010102603

Abstract
Hyper-heuristics can be identified as methodologies that search the space generated by a finite set of low level heuristics for solving search problems. An iterative hyper-heuristic framework can be thought of as requiring a single candidate solution and multiple perturbation low level heuristics. An initially generated complete solution goes through two successive processes (heuristic selection and move acceptance) until a set of termination criteria is satisfied. A motivating goal of hyper-heuristic research is to create automated techniques that are applicable to a wide range of problems with different characteristics. Some previous studies show that different combinations of heuristic selection and move acceptance as hyper-heuristic components might yield different performances. This study investigates whether learning heuristic selection can improve the performance of a great deluge based hyper-heuristic using an examination timetabling problem as a case study.

Journal
International Journal of Applied Metaheuristic Computing: Volume 1, Issue 1

People

Professor Gabriela Ochoa

Professor, Computing Science