FP-AK-QIEA-R for Multi-Objective Optimization

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The Evolutionary Algorithms have main features like: population, evolutionary operations (crossover, mate, mutation and others). Most of them are based on randomness and follow a criteria using fitness like selector. The FP-AK-QIEA-R uses probability density function according to best of initial pop...

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Detalles Bibliográficos
Autor: Saire, JEC
Formato: objeto de conferencia
Fecha de Publicación:2016
Institución:Consejo Nacional de Ciencia Tecnología e Innovación
Repositorio:CONCYTEC-Institucional
Lenguaje:inglés
OAI Identifier:oai:repositorio.concytec.gob.pe:20.500.12390/1075
Enlace del recurso:https://hdl.handle.net/20.500.12390/1075
https://doi.org/10.1145/3022702.3022714
Nivel de acceso:acceso abierto
Materia:Herencia
Genética
Algoritmo
https://purl.org/pe-repo/ocde/ford#1.06.07
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spelling Publicationrp03045600Saire, JEC2024-05-30T23:13:38Z2024-05-30T23:13:38Z2016https://hdl.handle.net/20.500.12390/1075https://doi.org/10.1145/3022702.3022714433384100014The Evolutionary Algorithms have main features like: population, evolutionary operations (crossover, mate, mutation and others). Most of them are based on randomness and follow a criteria using fitness like selector. The FP-AK-QIEA-R uses probability density function according to best of initial population to sample new population and uses rewarding criteria to sample around the best of every iteration using cumulative density function estimated for Akima interpolation, it was used for mono-objective problems showing good results. The proposal uses the algorithm FP-AK-QIEA-R and add Pareto dominance to experiment with multi-objective problems. The performed experiments use some benchmark functions from the literature and initial results shows a promissory way for the algorithm.Consejo Nacional de Ciencia, Tecnología e Innovación Tecnológica - ConcytecengAssociation for Computing Machineryinfo:eu-repo/semantics/openAccessHerenciaGenética-1Algoritmo-1https://purl.org/pe-repo/ocde/ford#1.06.07-1FP-AK-QIEA-R for Multi-Objective Optimizationinfo:eu-repo/semantics/conferenceObjectreponame:CONCYTEC-Institucionalinstname:Consejo Nacional de Ciencia Tecnología e Innovacióninstacron:CONCYTEC20.500.12390/1075oai:repositorio.concytec.gob.pe:20.500.12390/10752024-05-30 16:00:59.128http://purl.org/coar/access_right/c_14cbinfo:eu-repo/semantics/closedAccessmetadata only accesshttps://repositorio.concytec.gob.peRepositorio Institucional CONCYTECrepositorio@concytec.gob.pe#PLACEHOLDER_PARENT_METADATA_VALUE#<Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="d4dd8e44-ca8f-4f13-a9fd-8e3f737d5d01"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>FP-AK-QIEA-R for Multi-Objective Optimization</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2016</PublicationDate> <DOI>https://doi.org/10.1145/3022702.3022714</DOI> <ISI-Number>433384100014</ISI-Number> <Authors> <Author> <DisplayName>Saire, JEC</DisplayName> <Person id="rp03045" /> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Association for Computing Machinery</DisplayName> <OrgUnit /> </Publisher> </Publishers> <Keyword>Herencia</Keyword> <Keyword>Genética</Keyword> <Keyword>Algoritmo</Keyword> <Abstract>The Evolutionary Algorithms have main features like: population, evolutionary operations (crossover, mate, mutation and others). Most of them are based on randomness and follow a criteria using fitness like selector. The FP-AK-QIEA-R uses probability density function according to best of initial population to sample new population and uses rewarding criteria to sample around the best of every iteration using cumulative density function estimated for Akima interpolation, it was used for mono-objective problems showing good results. The proposal uses the algorithm FP-AK-QIEA-R and add Pareto dominance to experiment with multi-objective problems. The performed experiments use some benchmark functions from the literature and initial results shows a promissory way for the algorithm.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication> -1
dc.title.none.fl_str_mv FP-AK-QIEA-R for Multi-Objective Optimization
title FP-AK-QIEA-R for Multi-Objective Optimization
spellingShingle FP-AK-QIEA-R for Multi-Objective Optimization
Saire, JEC
Herencia
Genética
Algoritmo
https://purl.org/pe-repo/ocde/ford#1.06.07
title_short FP-AK-QIEA-R for Multi-Objective Optimization
title_full FP-AK-QIEA-R for Multi-Objective Optimization
title_fullStr FP-AK-QIEA-R for Multi-Objective Optimization
title_full_unstemmed FP-AK-QIEA-R for Multi-Objective Optimization
title_sort FP-AK-QIEA-R for Multi-Objective Optimization
author Saire, JEC
author_facet Saire, JEC
author_role author
dc.contributor.author.fl_str_mv Saire, JEC
dc.subject.none.fl_str_mv Herencia
topic Herencia
Genética
Algoritmo
https://purl.org/pe-repo/ocde/ford#1.06.07
dc.subject.es_PE.fl_str_mv Genética
Algoritmo
dc.subject.ocde.none.fl_str_mv https://purl.org/pe-repo/ocde/ford#1.06.07
description The Evolutionary Algorithms have main features like: population, evolutionary operations (crossover, mate, mutation and others). Most of them are based on randomness and follow a criteria using fitness like selector. The FP-AK-QIEA-R uses probability density function according to best of initial population to sample new population and uses rewarding criteria to sample around the best of every iteration using cumulative density function estimated for Akima interpolation, it was used for mono-objective problems showing good results. The proposal uses the algorithm FP-AK-QIEA-R and add Pareto dominance to experiment with multi-objective problems. The performed experiments use some benchmark functions from the literature and initial results shows a promissory way for the algorithm.
publishDate 2016
dc.date.accessioned.none.fl_str_mv 2024-05-30T23:13:38Z
dc.date.available.none.fl_str_mv 2024-05-30T23:13:38Z
dc.date.issued.fl_str_mv 2016
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
format conferenceObject
dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/20.500.12390/1075
dc.identifier.doi.none.fl_str_mv https://doi.org/10.1145/3022702.3022714
dc.identifier.isi.none.fl_str_mv 433384100014
url https://hdl.handle.net/20.500.12390/1075
https://doi.org/10.1145/3022702.3022714
identifier_str_mv 433384100014
dc.language.iso.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Association for Computing Machinery
publisher.none.fl_str_mv Association for Computing Machinery
dc.source.none.fl_str_mv reponame:CONCYTEC-Institucional
instname:Consejo Nacional de Ciencia Tecnología e Innovación
instacron:CONCYTEC
instname_str Consejo Nacional de Ciencia Tecnología e Innovación
instacron_str CONCYTEC
institution CONCYTEC
reponame_str CONCYTEC-Institucional
collection CONCYTEC-Institucional
repository.name.fl_str_mv Repositorio Institucional CONCYTEC
repository.mail.fl_str_mv repositorio@concytec.gob.pe
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FP-AK-QIEA-R for Multi-Objective Optimization
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