Effective learning hyper-heuristics for the course timetabling problem
Jorge A. Soria-Alcaraz,
Gabriela Ochoa,
Jerry Swan,
Martin Carpio,
Hector Puga and
Edmund K. Burke
European Journal of Operational Research, 2014, vol. 238, issue 1, 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.
Keywords: Timetabling; Hyper-heuristics; Heuristics; Metaheuristics; Combinatorial optimization (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (8)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:238:y:2014:i:1:p:77-86
DOI: 10.1016/j.ejor.2014.03.046
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