@inproceedings{0a745c16d2aa4f6bbfb810856e8854b2,
title = "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 {\textquoteleft}unseen{\textquoteright} problems in addition to the six standard HyFlex problem domains.",
keywords = "adaptation, metaheuristic, move acceptance, optimisation, parameter control",
author = "Alhanof Almutairi and Ender {\"O}zcan and Ahmed Kheiri and Jackson, {Warren G.}",
note = "Publisher Copyright: {\textcopyright} The Author(s) 2016.; 31st International Symposium on Computer and Information Sciences, ISCIS 2016 ; Conference date: 27-10-2016 Through 28-10-2016",
year = "2016",
month = sep,
day = "24",
doi = "10.1007/978-3-319-47217-1_17",
language = "English",
isbn = "9783319472164",
series = "Communications in Computer and Information Science",
publisher = "Springer Cham",
pages = "154--162",
editor = "Tadeusz Czach{\'o}rski and Erol Gelenbe and Krzysztof Grochla and Ricardo Lent",
booktitle = "Computer and Information Sciences",
address = "Switzerland",
}