Effective learning hyper-heuristics for the course timetabling problem

• Published in: European journal of operational research Vol. 238; no. 1; pp. 77 - 86
• Main Authors: Soria-Alcaraz, Jorge A., Ochoa, Gabriela, Swan, Jerry, Carpio, Martin, Puga, Hector, Burke, Edmund K.
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01-10-2014; Elsevier Sequoia S.A
• Subjects: Algorithms; Combinatorial optimization; Decision making models; Dynamics; Education; Heuristic; Heuristics; Human; Hyper-heuristics; Iterative methods; Learning; Mathematical problems; Metaheuristics; Operators; Phases; State of the art; Studies; Timetabling; Timetabling; Hyper-heuristics; Combinatorial optimization; Metaheuristics; Heuristics
• ISSN: 0377-2217; 1872-6860
• DOI: 10.1016/j.ejor.2014.03.046

•Course timetabling constraints are converted into generic structures.•Two types of learning for operator selection are contrasted: online and offline.•An empirical approach for selecting the best operators from a pool is proposed.•Online learning shows competitive results, even producing new best-known solutions.•The proposed method achieves a balance between generality and effectiveness.
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.