Skip to main content

Research Repository

Advanced Search

Performance of selection hyper-heuristics on the extended HyFlex domains

Almutairi, Alhanof; Özcan, Ender; Kheiri, Ahmed; Jackson, Warren G.

Authors

Alhanof Almutairi

Ahmed Kheiri

Warren G. Jackson



Abstract

Selection hyper-heuristics perform search over the space of heuristics by mixing and controlling a predefined set of low level heuristics for solving computationally hard combinatorial optimisation problems. Being reusable methods, they are expected to be applicable to multiple problem domains, hence performing well in cross-domain search. HyFlex is a general purpose heuristic search API which separates the high level search control from the domain details enabling rapid development and performance comparison of heuristic search methods, particularly hyper-heuristics. In this study, the performance of six previously proposed selection hyper-heuristics are evaluated on three recently introduced extended HyFlex problem domains, namely 0–1 Knapsack, Quadratic Assignment and Max-Cut. The empirical results indicate the strong generalising capability of two adaptive selection hyper-heuristics which perform well across the ‘unseen’ problems in addition to the six standard HyFlex problem domains.

Citation

Almutairi, A., Özcan, E., Kheiri, A., & Jackson, W. G. (2016). Performance of selection hyper-heuristics on the extended HyFlex domains. In Computer and information sciences: 31st International Symposium, ISCIS 2016, Kraków, Poland, October 27–28, 2016, proceedings. Springer. https://doi.org/10.1007/978-3-319-47217-1_17

Acceptance Date Jul 13, 2016
Publication Date Sep 24, 2016
Deposit Date Oct 4, 2016
Publicly Available Date Oct 4, 2016
Electronic ISSN 1865-0929
Peer Reviewed Peer Reviewed
Issue 659
Series Title Communications in computer and information science
Book Title Computer and information sciences: 31st International Symposium, ISCIS 2016, Kraków, Poland, October 27–28, 2016, proceedings
ISBN 978-3-319-47217-1
DOI https://doi.org/10.1007/978-3-319-47217-1_17
Keywords Metaheuristic; Parameter control; Adaptation; Move acceptance; Optimisation
Public URL http://eprints.nottingham.ac.uk/id/eprint/37337
Publisher URL http://link.springer.com/chapter/10.1007%2F978-3-319-47217-1_17
Copyright Statement Copyright information regarding this work can be found at the following address: http://creativecommons.org/licenses/by/4.0

Files


chp%3A10.1007%2F978-3-319-47217-1_17.pdf (929 Kb)
PDF

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





You might also like



Downloadable Citations