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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Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
}
TY - GEN
T1 - A sequence-based selection hyper-heuristic utilising a hidden markov model
AU - Kheiri, Ahmed
AU - Keedwell, Ed
N1 - © 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
PY - 2015/7/11
Y1 - 2015/7/11
N2 - 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.
AB - 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.
KW - Computational design
KW - Cross-domain
KW - Hyper-heuristic
U2 - 10.1145/2739480.2754766
DO - 10.1145/2739480.2754766
M3 - Conference contribution/Paper
AN - SCOPUS:84963701876
SP - 417
EP - 424
BT - GECCO 2015 - Proceedings of the 2015 Genetic and Evolutionary Computation Conference
PB - Association for Computing Machinery, Inc
T2 - 16th Genetic and Evolutionary Computation Conference, GECCO 2015
Y2 - 11 July 2015 through 15 July 2015
ER -