Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/113670
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dc.contributor.author | Neumann, A. | - |
dc.contributor.author | Gao, W. | - |
dc.contributor.author | Doerr, C. | - |
dc.contributor.author | Neumann, F. | - |
dc.contributor.author | Wagner, M. | - |
dc.contributor.editor | Aguirre, H. | - |
dc.date.issued | 2018 | - |
dc.identifier.citation | Proceedings of the 2018 Genetic and Evolutionary Computation Conference (GECCO'18), 2018 / Aguirre, H. (ed./s), pp.991-998 | - |
dc.identifier.isbn | 9781450356183 | - |
dc.identifier.uri | http://hdl.handle.net/2440/113670 | - |
dc.description.abstract | Diversity plays a crucial role in evolutionary computation. While diversity has been mainly used to prevent the population of an evolutionary algorithm from premature convergence, the use of evolutionary algorithms to obtain a diverse set of solutions has gained increasing attention in recent years. Diversity optimization in terms of features on the underlying problem allows to obtain a better understanding of possible solutions to the problem at hand and can be used for algorithm selection when dealing with combinatorial optimization problems such as the Traveling Salesperson Problem. We consider discrepancy-based diversity optimization approaches for evolving diverse sets of images as well as instances of the Traveling Salesperson problem where a local search is not able to find near optimal solutions. Our experimental investigations comparing three diversity optimization approaches show that a discrepancy-based diversity optimization approach using a tie-breaking rule based on weighted differences to surrounding feature points provides the best results in terms of the star discrepancy measure. | - |
dc.description.statementofresponsibility | Aneta Neumann, Wanru Gao, Carola Doerr, Frank Neumann, Markus Wagner | - |
dc.language.iso | en | - |
dc.publisher | Association for Computing Machinery | - |
dc.rights | © 2018 Copyright held by the owner/author(s). Publication rights licensed to Association for Computing Machinery. | - |
dc.source.uri | https://dl.acm.org/doi/proceedings/10.1145/3205455 | - |
dc.subject | Diversity; evolutionary algorithms; features | - |
dc.title | Discrepancy-based evolutionary diversity optimization | - |
dc.type | Conference paper | - |
dc.contributor.conference | Genetic and Evolutionary Computation Conference (GECCO) (15 Jul 2018 - 19 Jul 2018 : Kyoto, Japan) | - |
dc.identifier.doi | 10.1145/3205455.3205532 | - |
dc.publisher.place | New York, NY | - |
dc.relation.grant | http://purl.org/au-research/grants/arc/DP140103400 | - |
dc.relation.grant | http://purl.org/au-research/grants/arc/DP160102401 | - |
pubs.publication-status | Published | - |
dc.identifier.orcid | Neumann, A. [0000-0002-0036-4782] | - |
dc.identifier.orcid | Gao, W. [0000-0002-7805-0919] | - |
dc.identifier.orcid | Neumann, F. [0000-0002-2721-3618] | - |
dc.identifier.orcid | Wagner, M. [0000-0002-3124-0061] | - |
Appears in Collections: | Aurora harvest 3 Computer Science publications |
Files in This Item:
File | Description | Size | Format | |
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hdl_113670.pdf | Accepted Version | 2.41 MB | Adobe PDF | View/Open |
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