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

Ender Özcan, Mustafa Misir, Gabriela Ochoa, Edmund K. Burke

Source Title: International Journal of Applied Metaheuristic Computing (IJAMC)1(1)

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MLA

Özcan, Ender, et al. "A Reinforcement Learning - Great-Deluge Hyper-Heuristic for Examination Timetabling." IJAMC vol.1, no.1 2010: pp.39-59. http://doi.org/10.4018/jamc.2010102603

APA

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

Chicago

Özcan, Ender, et al. "A Reinforcement Learning - Great-Deluge Hyper-Heuristic for Examination Timetabling," International Journal of Applied Metaheuristic Computing (IJAMC) 1, no.1: 39-59. http://doi.org/10.4018/jamc.2010102603

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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.

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

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