Performance of selection hyper-heuristics on the extended HyFlex domains

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: https://nottingham-repository.worktribe.com/output/809518
• Publisher URL: http://link.springer.com/chapter/10.1007%2F978-3-319-47217-1_17