Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Publication date | 5/12/2012 |
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Host publication | 2012 12th UK Workshop on Computational Intelligence, UKCI 2012 |
Publisher | IEEE |
Pages | 1-8 |
Number of pages | 8 |
ISBN (electronic) | 9781467343923 |
ISBN (print) | 9781467343916 |
<mark>Original language</mark> | English |
Event | 2012 12th UK Workshop on Computational Intelligence, UKCI 2012 - Edinburgh, United Kingdom Duration: 5/09/2012 → 7/09/2012 |
Conference | 2012 12th UK Workshop on Computational Intelligence, UKCI 2012 |
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Country/Territory | United Kingdom |
City | Edinburgh |
Period | 5/09/12 → 7/09/12 |
Conference | 2012 12th UK Workshop on Computational Intelligence, UKCI 2012 |
---|---|
Country/Territory | United Kingdom |
City | Edinburgh |
Period | 5/09/12 → 7/09/12 |
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.