|Appears in Collections:||Computing Science and Mathematics Journal Articles|
|Peer Review Status:||Refereed|
|Title:||Effective learning hyper-heuristics for the course timetabling problem|
|Authors:||Soria-Alcaraz, Jorge A|
|Citation:||Soria-Alcaraz JA, Ochoa G, Swan J, Carpio M, Puga H & Burke E (2014) Effective learning hyper-heuristics for the course timetabling problem, European Journal of Operational Research, 238 (1), pp. 77-86.|
|Abstract:||Course timetabling is an important and recurring administrative activity in most educational institutions. This article combines a general modeling methodology with effective learning hyper-heuristics to solve this problem. The proposed hyper-heuristics are based on an iterated local search procedure that autonomously combines a set of move operators. Two types of learning for operator selection are contrasted: a static (offline) approach, with a clear distinction between training and execution phases; and a dynamic approach that learns on the fly. The resulting algorithms are tested over the set of real-world instances collected by the first and second International Timetabling competitions. The dynamic scheme statistically outperforms the static counterpart, and produces competitive results when compared to the state-of-the-art, even producing a new best-known solution. Importantly, our study illustrates that algorithms with increased autonomy and generality can outperform human designed problem-specific algorithms.|
|Rights:||This item has been embargoed for a period. During the embargo 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. Accepted refereed manuscript of: Soria-Alcaraz JA, Ochoa G, Swan J, Carpio M, Puga H & Burke E (2014) Effective learning hyper-heuristics for the course timetabling problem, European Journal of Operational Research, 238 (1), pp. 77-86. DOI: 10.1016/j.ejor.2014.03.046 © 2015, Elsevier. Licensed under the Creative Commons Attribution- NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/|
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