A greedy gradient-simulated annealing selection hyper-heuristic

Kalender, Murat and Kheiri, Ahmed and Özcan, Ender and Burke, Edmund K. (2013) A greedy gradient-simulated annealing selection hyper-heuristic. Soft Computing, 17 (12). pp. 2279-2292. ISSN 1433-7479

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Abstract

Educational timetabling problem is a challenging real world problem which has been of interest to many researchers and practitioners. There are many variants of this problem which mainly require scheduling of events and resources under various constraints. In this study, a curriculum based course timetabling problem at Yeditepe University is described and an iterative selection hyper-heuristic is presented as a solution method. A selection hyper-heuristic as a high level methodology operates on the space formed by a fixed set of low level heuristics which operate directly on the space of solutions. The move acceptance and heuristic selection methods are the main components of a selection hyper-heuristic. The proposed hyper-heuristic in this study combines a simulated annealing move acceptance method with a learning heuristic selection method and manages a set of low level constraint oriented heuristics. A key goal in hyper-heuristic research is to build low cost methods which are general and can be reused on unseen problem instances as well as other problem domains desirably with no additional human expert intervention. Hence, the proposed method is additionally applied to a high school timetabling problem, as well as six other problem domains from a hyper-heuristic benchmark to test its level of generality. The empirical results show that our easy-to-implement hyper-heuristic is effective in solving the Yeditepe course timetabling problem. Moreover, being sufficiently general, it delivers a reasonable performance across different problem domains.

Item Type: Journal Article
Journal or Publication Title: Soft Computing
Additional Information: The final publication is available at Springer via http://dx.doi.org/10.1007/s00500-013-1096-5
Uncontrolled Keywords: /dk/atira/pure/subjectarea/asjc/1700/1712
Subjects:
Departments: Lancaster University Management School > Management Science
ID Code: 123875
Deposited By: ep_importer_pure
Deposited On: 06 Mar 2018 14:40
Refereed?: Yes
Published?: Published
Last Modified: 20 Feb 2020 03:26
URI: https://eprints.lancs.ac.uk/id/eprint/123875

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