Final published version
Licence: CC BY: Creative Commons Attribution 4.0 International License
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Publication date | 24/09/2016 |
---|---|
Host publication | Computer and Information Sciences - 31st International Symposium, ISCIS 2016, Proceedings |
Editors | Ricardo Lent, Erol Gelenbe, Tadeusz Czachórski, Krzysztof Grochla |
Publisher | Springer Verlag |
Pages | 154-162 |
Number of pages | 9 |
ISBN (electronic) | 9783319472171 |
ISBN (print) | 9783319472164 |
<mark>Original language</mark> | English |
Event | 31st International Symposium on Computer and Information Sciences, ISCIS 2016 - Kraków, Poland Duration: 27/10/2016 → 28/10/2016 |
Conference | 31st International Symposium on Computer and Information Sciences, ISCIS 2016 |
---|---|
Country/Territory | Poland |
City | Kraków |
Period | 27/10/16 → 28/10/16 |
Name | Communications in Computer and Information Science |
---|---|
Volume | 659 |
ISSN (Print) | 1865-0929 |
Conference | 31st International Symposium on Computer and Information Sciences, ISCIS 2016 |
---|---|
Country/Territory | Poland |
City | Kraków |
Period | 27/10/16 → 28/10/16 |
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.