Conference Proceedings
Information Theoretic Classification of Problems for Metaheuristics
Kent CB Steer, Andrew Wirth, Saman K Halgamuge, X Li (ed.), M Kirley (ed.), M Zhang (ed.), D Green (ed.), V Ciesielski (ed.), H Abbass (ed.), Z Michalewicz (ed.), T Hendtlass (ed.), K Deb (ed.), KC Tan (ed.), J Branke (ed.), Y Shi (ed.)
SIMULATED EVOLUTION AND LEARNING, PROCEEDINGS | SPRINGER-VERLAG BERLIN | Published : 2008
Abstract
This paper proposes a model for metaheuristic research which recognises the need to match algorithms to problems. An empirical approach to producing a mapping from problems to algorithms is presented. This mapping, if successful, will encapsulate the knowledge gained from the application of metaheuristics to the spectrum of real problems. Information theoretic measures are suggested as a means of associating a dominant algorithm with a set of problems. © 2008 Springer Berlin Heidelberg.