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

Citation

Burke E, MacCarthy BL, Petrovic S & Qu R (2003) Knowledge discovery in a hyper-heuristic for course timetabling using case-based reasoning. In: Burke E & De Causmaecker P (eds.) Practice and Theory of Automated Timetabling IV: 4th International Conference, PATAT 2002, Gent, Belgium, August 21-23, 2002. Selected Revised Papers. Lecture Notes in Computer Science, 2740. 4th International Conference, PATAT 2002: Practice and Theory of Automated Timetabling IV, Gent, Belgium, 21.08.2002-23.08.2002. Berlin Heidelberg: Springer, pp. 276-287. http://link.springer.com/chapter/10.1007%2F978-3-540-45157-0_18; https://doi.org/10.1007/978-3-540-45157-0_18

Abstract
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 considerable promise for future work in this area.