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A modified choice function hyper-heuristic controlling unary and binary operators

Drake, John H.; Özcan, Ender; Burke, Edmund K.

Authors

John H. Drake

Edmund K. Burke



Abstract

Hyper-heuristics are a class of high-level search methodologies which operate on a search space of low-level heuristics or components, rather than on solutions directly. Traditional iterative selection hyper-heuristics rely on two key components, a heuristic selection method and a move acceptance criterion. Choice Function heuristic selection scores heuristics based on a combination of three measures, selecting the heuristic with the highest score. Modified Choice Function heuristic selection is a variant of the Choice Function which emphasises intensification over diversification within the heuristic search process. Previous work has shown that improved results are possible in some problem domains when using Modified Choice Function heuristic selection over the classic Choice Function, however in most of these cases crossover low-level heuristics (operators) are omitted. In this paper, we introduce crossover low-level heuristics into a Modified Choice Function selection hyper-heuristic and present results over six problem domains. It is observed that although on average there is an increase in performance when using crossover low-level heuristics, the benefit of using crossover can vary on a per-domain or per-instance basis.

Publication Date May 25, 2015
Peer Reviewed Peer Reviewed
Book Title 2015 IEEE Congress on Evolutionary Computation (CEC)
APA6 Citation Drake, J. H., Özcan, E., & Burke, E. K. (2015). A modified choice function hyper-heuristic controlling unary and binary operators. In 2015 IEEE Congress on Evolutionary Computation (CEC)doi:10.1109/CEC.2015.7257315
DOI https://doi.org/10.1109/CEC.2015.7257315
Publisher URL http://dx.doi.org/10.1109/CEC.2015.7257315
Copyright Statement Copyright information regarding this work can be found at the following address: http://creativecommons.org/licenses/by-nc-nd/4.0
Additional Information Published in: 2015 IEEE Congress on Evolutionary Computation (CEC2015) proceedings, 25-28 May 2015, Sendai, Japan. IEEE, 2015, ISBN 9781479974924, pp. 3389-3396.
© 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

doi:10.1109/CEC.2015.7257315

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Copyright Statement
Copyright information regarding this work can be found at the following address: http://creativecommons.org/licenses/by-nc-nd/4.0





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