Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/104753
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Type: Conference paper
Title: Feature-based diversity optimization for problem instance classification
Author: Gao, W.
Nallaperuma, S.
Neumann, F.
Citation: Lecture Notes in Artificial Intelligence, 2016 / Handl, J., Hart, E., Lewis, P.R., LopezIbanez, M., Ochoa, G., Paechter, B. (ed./s), vol.9921 LNCS, pp.869-879
Publisher: Springer International Publishing
Issue Date: 2016
Series/Report no.: Lecture Notes on Computer Science; 9921
ISBN: 3319458221
9783319458229
ISSN: 0302-9743
1611-3349
Conference Name: 14th International Conference on Parallel Problem Solving from Nature (PPSN 2016) (17 Sep 2016 - 21 Sep 2016 : Edinburgh, UK)
Editor: Handl, J.
Hart, E.
Lewis, P.R.
LopezIbanez, M.
Ochoa, G.
Paechter, B.
Statement of
Responsibility: 
Wanru Gao, Samadhi Nallaperuma, and Frank Neumann
Abstract: Understanding the behaviour of heuristic search methods is a challenge. This even holds for simple local search methods such as 2-OPT for the Traveling Salesperson problem. In this paper, we present a general framework that is able to construct a diverse set of instances that are hard or easy for a given search heuristic. Such a diverse set is obtained by using an evolutionary algorithm for constructing hard or easy instances that are diverse with respect to different features of the underlying problem. Examining the constructed instance sets, we show that many combinations of two or three features give a good classification of the TSP instances in terms of whether they are hard to be solved by 2-OPT.
Description: Parallel Problem Solving from Nature – PPSN XIV
Rights: © Springer International Publishing AG 2016
DOI: 10.1007/978-3-319-45823-6_81
Grant ID: http://purl.org/au-research/grants/arc/DP140103400
Published version: https://doi.org/10.1007/978-3-319-45823-6
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Computer Science publications

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