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Hybridising heuristics within an estimation distribution algorithm for examination timetabling

Qu, Rong; Pham, Duc Nam Trung; Bai, Ruibin; Kendall, Graham

Authors

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RONG QU rong.qu@nottingham.ac.uk
Associate Professor

Duc Nam Trung Pham

Ruibin Bai



Abstract

This paper presents a hybrid hyper-heuristic approach based on estimation distribution algorithms. The main motivation is to raise the level of generality for search methodologies. The objective of the hyper-heuristic is to produce solutions of acceptable quality for a number of optimisation problems. In this work, we demonstrate the generality through experimental results for different variants of exam timetabling problems. The hyper-heuristic represents an automated constructive method that searches for heuristic choices from a given set of low-level heuristics based only on non-domain-specific knowledge. The high-level search methodology is based on a simple estimation distribution algorithm. It is capable of guiding the search to select appropriate heuristics in different problem solving situations. The probability distribution of low-level heuristics at different stages of solution construction can be used to measure their effectiveness and possibly help to facilitate more intelligent hyper-heuristic search methods.

Journal Article Type Article
Publication Date 2015-06
Journal Applied Intelligence
Print ISSN 0924-669X
Electronic ISSN 1573-7497
Publisher Springer Verlag
Peer Reviewed Peer Reviewed
Volume 42
Issue 4
Pages 679-693
APA6 Citation Qu, R., Pham, D. N. T., Bai, R., & Kendall, G. (2015). Hybridising heuristics within an estimation distribution algorithm for examination timetabling. Applied Intelligence, 42(4), 679-693. https://doi.org/10.1007/s10489-014-0615-0
DOI https://doi.org/10.1007/s10489-014-0615-0
Publisher URL http://link.springer.com/article/10.1007%2Fs10489-014-0615-0
Copyright Statement Copyright information regarding this work can be found at the following address: http://eprints.nottingh.../end_user_agreement.pdf
Additional Information The final publication is available at Springer via http://dx.doi.org/10.1007/s10489-014-0615-0.

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Copyright Statement
Copyright information regarding this work can be found at the following address: http://eprints.nottingham.ac.uk/end_user_agreement.pdf





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