A genetic programming approach to the generation of hyper-heuristics for the uncapacitated examination timetabling problem

Authors:
Nelishia Pillay;Wolfgang Banzhaf
Affiliations:
School of Computer Science, Univesity of KwaZulu-Natal, Pietermaritzburg, KwaZulu-Natal, South Africa;Department of Computer Science, Memorial University of Newfoundland, St. John's, NL, Canada
Venue:
EPIA'07 Proceedings of the aritficial intelligence 13th Portuguese conference on Progress in artificial intelligence
Year:
2007

Citing 5
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Applied Intelligence

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Abstract

Research in the field of examination timetabling has developed in two directions. The first looks at applying various methodologies to induce examination timetables. The second takes an indirect approach to the problem and examines the generation of heuristics or combinations of heuristics, i.e. hyper-heuristics, to be used in the construction of examination timetables. The study presented in this paper focuses on the latter area. This paper presents a first attempt at using genetic programming for the evolution of hyper-heuristics for the uncapacitated examination timetabling problem. The system has been tested on 9 benchmark examination timetabling problems. Clash-free timetables were found for all 9 nine problems. Furthermore, the performance of the genetic programming system is comparable to, and in a number of cases has produced better quality timetables, than other search algorithms used to evolve hyper-heuristics for this set of problems.