A greedy gradient-simulated annealing hyper-heuristic for a curriculum-based course timetabling problem

Kalender, Murat, Kheiri, Ahmed ORCID: https://orcid.org/0000-0002-6716-2130, Ozcan, Ender and Burke, Edmund K. 2012. A greedy gradient-simulated annealing hyper-heuristic for a curriculum-based course timetabling problem. Presented at: 12th UK Workshop on Computational Intelligence (UKCI), Edinburgh, UK, 5-7 September 2012. Computational Intelligence (UKCI), 2012 12th UK Workshop on. IEEE, pp. 1-8. 10.1109/UKCI.2012.6335754

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Abstract

The course timetabling problem is a well known constraint optimization problem which has been of interest to researchers as well as practitioners. Due to the NP-hard nature of the problem, the traditional exact approaches might fail to find a solution even for a given instance. Hyper-heuristics which search the space of heuristics for high quality solutions are alternative methods that have been increasingly used in solving such problems. In this study, a curriculum based course timetabling problem at Yeditepe University is described. An improvement oriented heuristic selection strategy combined with a simulated annealing move acceptance as a hyper-heuristic utilizing a set of low level constraint oriented neighbourhood heuristics is investigated for solving this problem. The proposed hyper-heuristic was initially developed to handle a variety of problems in a particular domain with different properties considering the nature of the low level heuristics. On the other hand, a goal of hyper-heuristic development is to build methods which are general. Hence, the proposed hyper-heuristic is applied to six other problem domains and its performance is compared to different state-of-the-art hyper-heuristics to test its level of generality. The empirical results show that the proposed method is sufficiently general and powerful.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Publication
Status:	Published
Schools:	Schools > Mathematics
Subjects:	Q Science > QA Mathematics
Publisher:	IEEE
ISBN:	9781467343916
Last Modified:	31 Oct 2022 10:41
URI:	https://orca.cardiff.ac.uk/id/eprint/85724

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