Performance of selection hyper-heuristics on the extended HyFlex domains

Almutairi, Alhanof, Özcan, Ender, Kheiri, Ahmed and Jackson, Warren 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. Communications in computer and information science (659). Springer, pp. 154-162. ISBN 978-3-319-47217-1

Full text not available from this repository.


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.

Item Type: Book Section
Keywords: Metaheuristic; Parameter control; Adaptation; Move acceptance; Optimisation
Schools/Departments: University of Nottingham, UK > Faculty of Science > School of Computer Science
Identification Number:
Depositing User: Ozcan, Dr Ender
Date Deposited: 04 Oct 2016 11:09
Last Modified: 04 May 2020 18:09

Actions (Archive Staff Only)

Edit View Edit View