Identificador persistente para citar o vincular este elemento:
http://hdl.handle.net/10553/72378
Campo DC | Valor | idioma |
---|---|---|
dc.contributor.author | Salazar Aponte, Daniel E. | en_US |
dc.contributor.author | Rocco S, Claudio M. | en_US |
dc.contributor.author | Galván, Blas | en_US |
dc.date.accessioned | 2020-05-13T16:30:58Z | - |
dc.date.available | 2020-05-13T16:30:58Z | - |
dc.date.issued | 2010 | en_US |
dc.identifier.isbn | 978-3-642-01019-4 | en_US |
dc.identifier.issn | 0302-9743 | en_US |
dc.identifier.other | Scopus | - |
dc.identifier.other | WoS | - |
dc.identifier.uri | http://hdl.handle.net/10553/72378 | - |
dc.description.abstract | In this article we present a methodological framework entitled 'Analysis of Uncertainty and Robustness in Evolutionary Optimization' or AUREO for short. This methodology was developed as a diagnosis tool to analyze the characteristics of the decision-making problems to be solved with Multi-Objective Evolutionary Algorithms (MOEA) in order to: 1) determine the mathematical program that represents best the current problem in terms of the available information, and 2) to help the design or adaptation of the MOEA meant to solve the mathematical program. Regarding the first point, the different versions of decision-making problems in the presence of uncertainty are reduced to a few classes, while for the second point possible configurations of MOEA are suggested in terms of the type of uncertainty and the theory used to represent it. Finally, the AUREO has been introduced and tested successfully in different applications in [1]. | en_US |
dc.language | eng | en_US |
dc.relation.ispartof | Lecture Notes in Computer Science | en_US |
dc.source | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) [ISSN 0302-9743], v. 5467 LNCS, p. 51-65, (Diciembre 2010) | en_US |
dc.subject | 120304 Inteligencia artificial | en_US |
dc.subject.other | Multiobjective Optimization | en_US |
dc.subject.other | Mean-Value | en_US |
dc.subject.other | Variance | en_US |
dc.title | On uncertainty and robustness in evolutionary optimization-based MCDM | en_US |
dc.type | info:eu-repo/semantics/conferenceObject | en_US |
dc.type | ConferenceObject | en_US |
dc.relation.conference | 5th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2009 | en_US |
dc.identifier.doi | 10.1007/978-3-642-01020-0_9 | en_US |
dc.identifier.scopus | 78650742261 | - |
dc.identifier.isi | 000265784100004 | - |
dc.contributor.authorscopusid | 56240751500 | - |
dc.contributor.authorscopusid | 7004508307 | - |
dc.contributor.authorscopusid | 8704390300 | - |
dc.identifier.eissn | 1611-3349 | - |
dc.description.lastpage | 65 | en_US |
dc.description.firstpage | 51 | en_US |
dc.relation.volume | 5467 LNCS | en_US |
dc.investigacion | Ingeniería y Arquitectura | en_US |
dc.type2 | Actas de congresos | en_US |
dc.contributor.daisngid | 1400577 | - |
dc.contributor.daisngid | 10849402 | - |
dc.contributor.daisngid | 1678121 | - |
dc.description.numberofpages | 4 | en_US |
dc.identifier.eisbn | 978-3-642-01020-0 | - |
dc.utils.revision | Sí | en_US |
dc.contributor.wosstandard | WOS:Aponte, DE | - |
dc.contributor.wosstandard | WOS:Rocco, CM | - |
dc.contributor.wosstandard | WOS:Galvan, B | - |
dc.date.coverdate | Diciembre 2010 | en_US |
dc.identifier.conferenceid | events120670 | - |
dc.identifier.ulpgc | Sí | es |
item.grantfulltext | none | - |
item.fulltext | Sin texto completo | - |
crisitem.author.dept | GIR SIANI: Computación Evolutiva y Aplicaciones | - |
crisitem.author.dept | IU Sistemas Inteligentes y Aplicaciones Numéricas | - |
crisitem.author.parentorg | IU Sistemas Inteligentes y Aplicaciones Numéricas | - |
crisitem.author.fullName | Galvan Gonzalez,Blas Jose | - |
crisitem.event.eventsstartdate | 07-04-2009 | - |
crisitem.event.eventsenddate | 10-04-2009 | - |
Colección: | Actas de congresos |
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