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    Rights statement: © ACM, 2015. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in GECCO '15 Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation http://dx.doi.org/10.1145/2739480.2754766

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A sequence-based selection hyper-heuristic utilising a hidden markov model

Research output: Contribution in Book/Report/ProceedingsConference contribution

Published
Publication date11/07/2015
Host publicationGECCO 2015 - Proceedings of the 2015 Genetic and Evolutionary Computation Conference
PublisherAssociation for Computing Machinery, Inc
Pages417-424
Number of pages8
ISBN (Electronic)9781450334723
<mark>Original language</mark>English
Event16th Genetic and Evolutionary Computation Conference, GECCO 2015 - Madrid, Spain

Conference

Conference16th Genetic and Evolutionary Computation Conference, GECCO 2015
CountrySpain
CityMadrid
Period11/07/1515/07/15

Conference

Conference16th Genetic and Evolutionary Computation Conference, GECCO 2015
CountrySpain
CityMadrid
Period11/07/1515/07/15

Abstract

Selection hyper-heuristics are optimisation methods that operate at the level above traditional (meta-)heuristics. Their task is to evaluate low level heuristics and determine which of these to apply at a given point in the optimisation process. Traditionally this has been accomplished through the evaluation of individual or paired heuristics. In this work, we propose a hidden Markov model based method to analyse the performance of, and construct, longer sequences of low level heuristics to solve difficult problems. The proposed method is tested on the well known hyper-heuristic benchmark problems within the CHeSC 2011 competition and compared with a large number of algorithms in this domain. The empirical results show that the proposed hyper-heuristic is able to outperform the current best-in-class hyper-heuristic on these problems with minimal parameter tuning and so points the way to a new field of sequence-based selection hyper-heuristics.

Bibliographic note

© ACM, 2015. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in GECCO '15 Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation http://dx.doi.org/10.1145/2739480.2754766