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: | http://dx.doi.org/10.1007/978-3-319-45823-6_81 |
Appears in Collections: | Aurora harvest 3 Computer Science publications |
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RA_hdl_104753.pdf | Restricted Access | 780.08 kB | Adobe PDF | View/Open |
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