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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/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

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A sequence-based selection hyper-heuristic utilising a hidden markov model. / Kheiri, Ahmed; Keedwell, Ed.
GECCO 2015 - Proceedings of the 2015 Genetic and Evolutionary Computation Conference. Association for Computing Machinery, Inc, 2015. p. 417-424.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

Harvard

Kheiri, A & Keedwell, E 2015, A sequence-based selection hyper-heuristic utilising a hidden markov model. in GECCO 2015 - Proceedings of the 2015 Genetic and Evolutionary Computation Conference. Association for Computing Machinery, Inc, pp. 417-424, 16th Genetic and Evolutionary Computation Conference, GECCO 2015, Madrid, Spain, 11/07/15. https://doi.org/10.1145/2739480.2754766

APA

Kheiri, A., & Keedwell, E. (2015). A sequence-based selection hyper-heuristic utilising a hidden markov model. In GECCO 2015 - Proceedings of the 2015 Genetic and Evolutionary Computation Conference (pp. 417-424). Association for Computing Machinery, Inc. https://doi.org/10.1145/2739480.2754766

Vancouver

Kheiri A, Keedwell E. A sequence-based selection hyper-heuristic utilising a hidden markov model. In GECCO 2015 - Proceedings of the 2015 Genetic and Evolutionary Computation Conference. Association for Computing Machinery, Inc. 2015. p. 417-424 doi: 10.1145/2739480.2754766

Author

Kheiri, Ahmed ; Keedwell, Ed. / A sequence-based selection hyper-heuristic utilising a hidden markov model. GECCO 2015 - Proceedings of the 2015 Genetic and Evolutionary Computation Conference. Association for Computing Machinery, Inc, 2015. pp. 417-424

Bibtex

@inproceedings{3c6f5aac08e94ccf880aab024bcc8052,
title = "A sequence-based selection hyper-heuristic utilising a hidden markov model",
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.",
keywords = "Computational design, Cross-domain, Hyper-heuristic",
author = "Ahmed Kheiri and Ed Keedwell",
note = "{\textcopyright} 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; 16th Genetic and Evolutionary Computation Conference, GECCO 2015 ; Conference date: 11-07-2015 Through 15-07-2015",
year = "2015",
month = jul,
day = "11",
doi = "10.1145/2739480.2754766",
language = "English",
pages = "417--424",
booktitle = "GECCO 2015 - Proceedings of the 2015 Genetic and Evolutionary Computation Conference",
publisher = "Association for Computing Machinery, Inc",

}

RIS

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 -