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Effective learning hyper-heuristics for the course timetabling problem
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DOI：10.1016/j.ejor.2014.03.046.png)
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Abstract
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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. (C) 2014 Elsevier B.V. All rights reserved.
Keywords:
Timetabling
Hyper-heuristics
Heuristics
Metaheuristics
Combinatorial optimization
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