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

Author

Listed:
  • Ender Özcan

    (University of Nottingham, UK)

  • Mustafa Misir

    (Yeditepe University, Turkey)

  • Gabriela Ochoa

    (University of Nottingham, UK)

  • Edmund K. Burke

    (University of Nottingham, UK)

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.

Suggested Citation

  • Ender Özcan & Mustafa Misir & Gabriela Ochoa & Edmund K. Burke, 2010. "A Reinforcement Learning - Great-Deluge Hyper-Heuristic for Examination Timetabling," International Journal of Applied Metaheuristic Computing (IJAMC), IGI Global, vol. 1(1), pages 39-59, January.
    • Handle: RePEc:igg:jamc00:v:1:y:2010:i:1:p:39-59
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    Citations

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    Cited by:

    1. Syariza Abdul-Rahman & Edmund Burke & Andrzej Bargiela & Barry McCollum & Ender Özcan, 2014. "A constructive approach to examination timetabling based on adaptive decomposition and ordering," Annals of Operations Research, Springer, vol. 218(1), pages 3-21, July.
    2. Kallestad, Jakob & Hasibi, Ramin & Hemmati, Ahmad & Sörensen, Kenneth, 2023. "A general deep reinforcement learning hyperheuristic framework for solving combinatorial optimization problems," European Journal of Operational Research, Elsevier, vol. 309(1), pages 446-468.
    3. W. B. Yates & E. C. Keedwell, 2019. "An analysis of heuristic subsequences for offline hyper-heuristic learning," Journal of Heuristics, Springer, vol. 25(3), pages 399-430, June.
    4. Johnes, Jill, 2015. "Operational Research in education," European Journal of Operational Research, Elsevier, vol. 243(3), pages 683-696.
    5. Ahmed Kheiri, 2020. "Heuristic Sequence Selection for Inventory Routing Problem," Transportation Science, INFORMS, vol. 54(2), pages 302-312, March.
    6. Edmund K. Burke & Yuri Bykov, 2016. "An Adaptive Flex-Deluge Approach to University Exam Timetabling," INFORMS Journal on Computing, INFORMS, vol. 28(4), pages 781-794, November.
    7. Surafel Luleseged Tilahun & Mohamed A. Tawhid, 2019. "Swarm hyperheuristic framework," Journal of Heuristics, Springer, vol. 25(4), pages 809-836, October.

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