Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/127219
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dc.contributor.authorBossek, J.en
dc.contributor.authorKerschke, P.en
dc.contributor.authorNeumann, A.en
dc.contributor.authorWagner, M.en
dc.contributor.authorNeumann, F.en
dc.contributor.authorTrautmann, H.en
dc.date.issued2019en
dc.identifier.citationFOGA '19: Proceedings of the 15th ACM/SIGEVO Conference on Foundations of Genetic Algorithms, 2019 / pp.58-71en
dc.identifier.isbn9781450362542en
dc.identifier.urihttp://hdl.handle.net/2440/127219-
dc.description.abstractEvolutionary algorithms have successfully been applied to evolve problem instances that exhibit a significant difference in performance for a given algorithm or a pair of algorithms inter alia for the Traveling Salesperson Problem (TSP). Creating a large variety of instances is crucial for successful applications in the blooming field of algorithm selection. In this paper, we introduce new and creative mutation operators for evolving instances of the TSP. We show that adopting those operators in an evolutionary algorithm allows for the generation of benchmark sets with highly desirable properties: (1) novelty by clear visual distinction to established benchmark sets in the field, (2) visual and quantitative diversity in the space of TSP problem characteristics, and (3) significant performance differences with respect to the restart versions of heuristic state-of-the-art TSP solvers EAX and LKH. The important aspect of diversity is addressed and achieved solely by the proposed mutation operators and not enforced by explicit diversity preservation.en
dc.description.statementofresponsibilityJakob Bossek, Pascal Kerschke, Aneta Neumann, Markus Wagner, Frank Neumann, Heike Trautmannen
dc.language.isoenen
dc.publisherAssociation for Computing Machineryen
dc.rights© 2019 Copyright held by the owner/author(s). Publication rights licensed to the Association for Computing Machinery.en
dc.source.urihttps://doi.org/10.1145/3299904en
dc.titleEvolving diverse TSP instances by means of novel and creative mutation operatorsen
dc.typeConference paperen
dc.identifier.rmid1000011415en
dc.contributor.conferenceFoundations of Genetic Algorithms (FOGA) (26 Aug 2019 - 29 Aug 2019 : Potsdam, Germany)en
dc.identifier.doi10.1145/3299904.3340307en
dc.publisher.placeonlineen
dc.relation.granthttp://purl.org/au-research/grants/arc/DP190103894en
dc.identifier.pubid494576-
pubs.library.collectionComputer Science publicationsen
pubs.library.teamDS05en
pubs.verification-statusVerifieden
pubs.publication-statusPublisheden
dc.identifier.orcidBossek, J. [0000-0002-4121-4668]en
dc.identifier.orcidNeumann, A. [0000-0002-0036-4782]en
dc.identifier.orcidWagner, M. [0000-0002-3124-0061]en
dc.identifier.orcidNeumann, F. [0000-0002-2721-3618]en
Appears in Collections:Computer Science publications

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