Please use this identifier to cite or link to this item:
Appears in Collections:Computing Science and Mathematics Conference Papers and Proceedings
Author(s): Kalender, Murat
Kheiri, Ahmed
Ozcan, Ender
Burke, Edmund
Contact Email:
Title: A greedy gradient-simulated annealing hyper-heuristic for a curriculum-based course timetabling problem
Editor(s): De, Wilde P
Coghill, GM
Kononova, AV
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.
Issue Date: 2012
Conference Name: 2012 12th UK Workshop on Computational Intelligence (UKCI)
Conference Dates: 2012-09-05T00:00:00Z
Conference Location: Edinburgh
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.
Status: Publisher version
Rights: The publisher does not allow this work to be made publicly available in this Repository. Please use the Request a Copy feature at the foot of the Repository record to request a copy directly from the author. You can only request a copy if you wish to use this work for your own research or private study.

Files in This Item:
File Description SizeFormat 
A greedy gradient-simulated annealing hyper-heuristic.pdf195.27 kBAdobe PDFUnder Permanent Embargo    Request a copy

Note: If any of the files in this item are currently embargoed, you can request a copy directly from the author by clicking the padlock icon above. However, this facility is dependent on the depositor still being contactable at their original email address.

This item is protected by original copyright

Items in the Repository are protected by copyright, with all rights reserved, unless otherwise indicated.

If you believe that any material held in STORRE infringes copyright, please contact providing details and we will remove the Work from public display in STORRE and investigate your claim.