Evolving hyper-heuristics for the uncapacitated examination timetabling problem

Abstract

This paper reports on the use of an evolutionary algorithm (EA) to search a space of heuristic combinations for the uncapacitated examination timetabling problem. The representation used by an EA has an effect on the difficulty of the search and hence the overall success of the system. The paper examines three different representations of heuristic combinations for this problem and compares their performance on a set of benchmark problems for the uncapacitated examination timetabling problem. The study has revealed that certain representations do result in a better performance and generalization of the hyper-heuristic. An EA-based hyper-heuristic combining the use of all three representations (CEA) was implemented and found to generalize better than the EA using each of the representations separately.

Keywords:

• hyper-heuristics
• uncapacitated examination timetabling problem
• evolutionary algorithms

Acknowledgements

The author would like to thank the reviewers for their helpful suggestions and comments.

Notes

1 The central limit theorem states that as the sample size increases the distribution of sample means tend towards a normal distribution (Moore and McCabe, 1993) and thus the samples means of a distribution are guaranteed to be normally distributed (even if the distribution is not) if sample sizes of at least 30 are used (Willemse, 1994).