|Appears in Collections:||Computing Science and Mathematics Conference Papers and Proceedings|
|Title:||A greedy gradient-simulated annealing hyper-heuristic for a curriculum-based course timetabling problem|
|Editor(s):||De, Wilde P|
|Citation:||Kalender M, Kheiri A, Ozcan E & Burke E (2012) A greedy gradient-simulated annealing hyper-heuristic for a curriculum-based course timetabling problem In: De Wilde P, Coghill GM, Kononova AV (ed.) 2012 12th UK Workshop on Computational Intelligence, UKCI 2012 , Red Hook, NY: IEEE. 2012 12th UK Workshop on Computational Intelligence (UKCI), 5.9.2012 - 7.9.2012, Edinburgh.|
|Conference Name:||2012 12th UK Workshop on Computational Intelligence (UKCI)|
|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.|
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