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Knowledge discovery in hyper-heuristic using case-based reasoning on course timetabling

Burke, Edmund; MacCarthy, Bart L.; Petrovic, Sanja; Qu, Rong


Edmund Burke

Bart L. MacCarthy

Professor of Operational Research

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Associate Professor


This paper presents a new hyper-heuristic method using Case-Based Reasoning (CBR) for solving course timetabling problems. The term Hyper-heuristics has recently been employed to refer to 'heuristics that choose heuristics' rather than heuristics that operate directly on given problems. One of the overriding motivations of hyper-heuristic methods is the attempt to develop techniques that can operate with greater generality than is currently possible. The basic idea behind this is that we maintain a case base of information about the most successful heuristics for a range of previous timetabling problems to predict the best heuristic for the new problem in hand using the previous knowledge. Knowledge discovery techniques are used to carry out the training on the CBR system to improve the system performance on the prediction. Initial results presented in this paper are good and we conclude by discussing the con-siderable promise for future work in this area.


Burke, E., MacCarthy, B. L., Petrovic, S., & Qu, R. (2002). Knowledge discovery in hyper-heuristic using case-based reasoning on course timetabling.

Conference Name International Conference on the Practice and Theory of Automated Timetabling
Publication Date Jan 1, 2002
Deposit Date Dec 12, 2005
Publicly Available Date Oct 9, 2007
Peer Reviewed Peer Reviewed
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