Information Theoretic Classification of Problems for Metaheuristics

Authors:
Kent C. Steer;Andrew Wirth;Saman K. Halgamuge
Affiliations:
The University of Melbourne, Parkville, Australia;The University of Melbourne, Parkville, Australia;The University of Melbourne, Parkville, Australia
Venue:
SEAL '08 Proceedings of the 7th International Conference on Simulated Evolution and Learning
Year:
2008

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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.