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

Ender Özcan, Mustafa Misir, Gabriela Ochoa and Edmund K. Burke
Additional contact information
Ender Özcan: University of Nottingham, UK
Mustafa Misir: Yeditepe University, Turkey
Gabriela Ochoa: University of Nottingham, UK
Edmund K. Burke: University of Nottingham, UK

International Journal of Applied Metaheuristic Computing (IJAMC), 2010, vol. 1, issue 1, 39-59

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.

Date: 2010
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