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

Kalender, Murat, Kheiri, Ahmed, 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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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: Mathematics
Subjects: Q Science > QA Mathematics
Publisher: IEEE
ISBN: 9781467343916
Last Modified: 08 Jan 2020 04:56

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