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https://hdl.handle.net/2440/66516
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DC Field | Value | Language |
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dc.contributor.author | Horoba, C. | - |
dc.contributor.author | Neumann, F. | - |
dc.contributor.editor | Garibay, I.I. | - |
dc.contributor.editor | Jansen, T. | - |
dc.contributor.editor | Wiegand, R.P. | - |
dc.contributor.editor | Wu, A.S. | - |
dc.date.issued | 2009 | - |
dc.identifier.citation | Proceedings of the tenth ACM SIGEVO workshop on Foundations of genetic algorithms (FOGA' 09), 2009: pp.79-86 | - |
dc.identifier.isbn | 9781605584140 | - |
dc.identifier.uri | http://hdl.handle.net/2440/66516 | - |
dc.description.abstract | Often the Pareto front of a multi-objective optimization problem grows exponentially with the problem size. In this case, it is not possible to compute the whole Pareto front efficiently and one is interested in good approximations. We consider how evolutionary algorithms can achieve such approximations by using different diversity mechanisms. We discuss some well-known approaches such as the density estimator and the "-dominance approach and point out how and when such mechanisms provably help to obtain good additive approximations of the Pareto-optimal set. | - |
dc.description.statementofresponsibility | Christian Horoba and Frank Neumann | - |
dc.language.iso | en | - |
dc.publisher | ACM Press | - |
dc.rights | Copyright 2009 ACM | - |
dc.source.uri | http://dx.doi.org/10.1145/1527125.1527137 | - |
dc.subject | Algorithms | - |
dc.subject | Performance | - |
dc.subject | Theory | - |
dc.title | Additive approximations of Pareto-optimal sets by evolutionary multi-objective algorithms | - |
dc.type | Conference paper | - |
dc.contributor.conference | ACM SIGEVO Workshop on Foundations of Genetic Algorithms (10th : 2009 : Orlando, Florida) | - |
dc.identifier.doi | 10.1145/1527125.1527137 | - |
dc.publisher.place | New York | - |
pubs.publication-status | Published | - |
dc.identifier.orcid | Neumann, F. [0000-0002-2721-3618] | - |
Appears in Collections: | Aurora harvest 5 Computer Science publications |
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