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.
CITATION STYLE
Steer, K. C. B., Wirth, A., & Halgamuge, S. K. (2008). Information theoretic classification of problems for metaheuristics. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5361 LNAI, pp. 319–328). https://doi.org/10.1007/978-3-540-89694-4_33
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