2003 | OriginalPaper | Chapter
Knowledge Discovery in a Hyper-heuristic for Course Timetabling Using Case-Based Reasoning
Authors : E. K. Burke, B. L. MacCarthy, S. Petrovic, R. Qu
Published in: Practice and Theory of Automated Timetabling IV
Publisher: Springer Berlin Heidelberg
Included in: Professional Book Archive
Activate our intelligent search to find suitable subject content or patents.
Select sections of text to find matching patents with Artificial Intelligence. powered by
Select sections of text to find additional relevant content using AI-assisted search. powered by
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.