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Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/127218
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dc.contributor.authorNeumann, A.-
dc.contributor.authorGao, W.-
dc.contributor.authorWagner, M.-
dc.contributor.authorNeumann, F.-
dc.contributor.editorLopezIbanez, M.-
dc.date.issued2018-
dc.identifier.citationProceedings of the Genetic and Evolutionary Computation Conference (GECCO '19), 2018 / LopezIbanez, M. (ed./s), vol.-, pp.837-845-
dc.identifier.isbn9781450361118-
dc.identifier.urihttp://hdl.handle.net/2440/127218-
dc.description.abstractEvolutionary diversity optimization aims to compute a set of solutions that are diverse in the search space or instance feature space, and where all solutions meet a given quality criterion. With this paper, we bridge the areas of evolutionary diversity optimization and evolutionary multi-objective optimization. We show how popular indicators frequently used in the area of multi-objective optimization can be used for evolutionary diversity optimization. Our experimental investigations for evolving diverse sets of TSP instances and images according to various features show that two of the most prominent multi-objective indicators, namely the hypervolume indicator and the inverted generational distance, provide excellent results in terms of visualization and various diversity indicators.-
dc.description.statementofresponsibilityAneta Neumann, Wanru Gao, Markus Wagner, Frank Neumann-
dc.language.isoen-
dc.publisherAssociation for Computing Machinery-
dc.rights© 2019 Copyright held by the owner/author(s). Publication rights licensed to the Association for Computing Machinery.-
dc.source.urihttps://dl.acm.org/doi/proceedings/10.1145/3321707-
dc.titleEvolutionary diversity optimization using multi-objective indicators-
dc.typeConference paper-
dc.contributor.conferenceGenetic and Evolutionary Computation Conference (GECCO) (13 Jul 2019 - 17 Jul 2019 : Prague, Czech Republic)-
dc.identifier.doi10.1145/3321707.3321796-
dc.publisher.placeNew York, NY-
dc.relation.granthttp://purl.org/au-research/grants/arc/DE160100850-
dc.relation.granthttp://purl.org/au-research/grants/arc/DP160102401-
dc.relation.granthttp://purl.org/au-research/grants/arc/DP190103894-
pubs.publication-statusPublished-
dc.identifier.orcidNeumann, A. [0000-0002-0036-4782]-
dc.identifier.orcidGao, W. [0000-0002-7805-0919]-
dc.identifier.orcidWagner, M. [0000-0002-3124-0061]-
dc.identifier.orcidNeumann, F. [0000-0002-2721-3618]-
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Computer Science publications

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