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https://hdl.handle.net/2440/126049
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dc.contributor.author | Osuna, E. | - |
dc.contributor.author | Neumann, F. | - |
dc.contributor.author | Gao, W. | - |
dc.contributor.author | Sudholt, D. | - |
dc.contributor.editor | Bosman, P.A.N. | - |
dc.date.issued | 2017 | - |
dc.identifier.citation | GECCO '17: Proceedings of the 2017 Genetic and Evolutionary Computation Conference, 2017 / Bosman, P.A.N. (ed./s), pp.553-560 | - |
dc.identifier.isbn | 9781450349208 | - |
dc.identifier.uri | http://hdl.handle.net/2440/126049 | - |
dc.description.abstract | Parent selection in evolutionary algorithms for multi-objective optimization is usually performed by dominance mechanisms or indicator functions that prefer non-dominated points, while the reproduction phase involves the application of diversity mechanisms or other methods to achieve a good spread of the population along the Pareto front. We propose to refine the parent selection on evolutionary multi-objective optimization with diversity-based metrics. The aim is to focus on individuals with a high diversity contribution located in poorly explored areas of the search space, so the chances of creating new non-dominated individuals are better than in highly populated areas. We show by means of rigorous runtime analysis that the use of diversity-based parent selection mechanisms in the Simple Evolutionary Multi-objective Optimiser (SEMO) and Global SEMO for the well known bi-objective functions OneMinMax and Lotz can significantly improve their performance. Our theoretical results are accompanied by additional experiments that show a correspondence between theory and empirical results. | - |
dc.description.statementofresponsibility | Edgar Covantes Osuna, Wanru Gao, Frank Neumann, Dirk Sudholt | - |
dc.language.iso | en | - |
dc.publisher | Association for Computing Machinery (ACM) | - |
dc.rights | © 2017 Copyright held by the owner/author(s). Publication rights licensed to ACM. | - |
dc.source.uri | http://dx.doi.org/10.1145/3071178.3080294 | - |
dc.subject | Parent selection, evolutionary algorithms, multi-objective optimization, diversity mechanisms, runtime analysis, theory | - |
dc.title | Speeding up evolutionary multi-objective optimisation through diversity-based parent selection | - |
dc.type | Conference paper | - |
dc.contributor.conference | Genetic and Evolutionary Computation Conference (GECCO) (15 Jul 2017 - 19 Jul 2017 : Berlin, Germany) | - |
dc.identifier.doi | 10.1145/3071178.3080294 | - |
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, F. [0000-0002-2721-3618] | - |
dc.identifier.orcid | Gao, W. [0000-0002-7805-0919] | - |
Appears in Collections: | Aurora harvest 8 Computer Science publications |
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