Hybridizing an immune artificial algorithm with epsilon constrained method

Autores
Aragón, Victoria S.; Esquivel, Susana Cecilia
Año de publicación
2012
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
In this paper, we present a modified version of an algorithm inspired on the T-Cell model, it is an artificial immune system (AIS), based on the process that suffers the T-Cell. The proposed algorithm is called TCEC (T-Cell Epsilon Constrained) due to it is increased with epsilon constrained method, for solving constrained (numerical) opti- mization problems. We validate our proposed approach with a set of 36 test functions provided for the CEC 2010 competition. We indirectly compare our results with respect to a version of the differential evolution algorithm. Our results show that TCEC can found feasible solutions on almost test functions with 10 and 30 decision variables.
Eje: Workshop Agentes y sistemas inteligentes (WASI)
Red de Universidades con Carreras en Informática (RedUNCI)
Materia
Ciencias Informáticas
Algorithms
Hybrid systems
Optimization
Intelligent agents
Artificial Immune System
Constrained Optimization Problem
Epsilon Constrained Method
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/23590
Acceder
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oai_identifier_str oai:sedici.unlp.edu.ar:10915/23590
network_acronym_str SEDICI
repository_id_str 1329
network_name_str SEDICI (UNLP)
spelling Hybridizing an immune artificial algorithm with epsilon constrained methodAragón, Victoria S.Esquivel, Susana CeciliaCiencias InformáticasAlgorithmsHybrid systemsOptimizationIntelligent agentsArtificial Immune SystemConstrained Optimization ProblemEpsilon Constrained MethodIn this paper, we present a modified version of an algorithm inspired on the T-Cell model, it is an artificial immune system (AIS), based on the process that suffers the T-Cell. The proposed algorithm is called TCEC (T-Cell Epsilon Constrained) due to it is increased with epsilon constrained method, for solving constrained (numerical) opti- mization problems. We validate our proposed approach with a set of 36 test functions provided for the CEC 2010 competition. We indirectly compare our results with respect to a version of the differential evolution algorithm. Our results show that TCEC can found feasible solutions on almost test functions with 10 and 30 decision variables.Eje: Workshop Agentes y sistemas inteligentes (WASI)Red de Universidades con Carreras en Informática (RedUNCI)2012-10info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdfhttp://sedici.unlp.edu.ar/handle/10915/23590enginfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/2.5/ar/Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-29T10:55:31Zoai:sedici.unlp.edu.ar:10915/23590Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 10:55:31.559SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Hybridizing an immune artificial algorithm with epsilon constrained method
title Hybridizing an immune artificial algorithm with epsilon constrained method
spellingShingle Hybridizing an immune artificial algorithm with epsilon constrained method
Aragón, Victoria S.
Ciencias Informáticas
Algorithms
Hybrid systems
Optimization
Intelligent agents
Artificial Immune System
Constrained Optimization Problem
Epsilon Constrained Method
title_short Hybridizing an immune artificial algorithm with epsilon constrained method
title_full Hybridizing an immune artificial algorithm with epsilon constrained method
title_fullStr Hybridizing an immune artificial algorithm with epsilon constrained method
title_full_unstemmed Hybridizing an immune artificial algorithm with epsilon constrained method
title_sort Hybridizing an immune artificial algorithm with epsilon constrained method
dc.creator.none.fl_str_mv Aragón, Victoria S.
Esquivel, Susana Cecilia
author Aragón, Victoria S.
author_facet Aragón, Victoria S.
Esquivel, Susana Cecilia
author_role author
author2 Esquivel, Susana Cecilia
author2_role author
dc.subject.none.fl_str_mv Ciencias Informáticas
Algorithms
Hybrid systems
Optimization
Intelligent agents
Artificial Immune System
Constrained Optimization Problem
Epsilon Constrained Method
topic Ciencias Informáticas
Algorithms
Hybrid systems
Optimization
Intelligent agents
Artificial Immune System
Constrained Optimization Problem
Epsilon Constrained Method
dc.description.none.fl_txt_mv In this paper, we present a modified version of an algorithm inspired on the T-Cell model, it is an artificial immune system (AIS), based on the process that suffers the T-Cell. The proposed algorithm is called TCEC (T-Cell Epsilon Constrained) due to it is increased with epsilon constrained method, for solving constrained (numerical) opti- mization problems. We validate our proposed approach with a set of 36 test functions provided for the CEC 2010 competition. We indirectly compare our results with respect to a version of the differential evolution algorithm. Our results show that TCEC can found feasible solutions on almost test functions with 10 and 30 decision variables.
Eje: Workshop Agentes y sistemas inteligentes (WASI)
Red de Universidades con Carreras en Informática (RedUNCI)
description In this paper, we present a modified version of an algorithm inspired on the T-Cell model, it is an artificial immune system (AIS), based on the process that suffers the T-Cell. The proposed algorithm is called TCEC (T-Cell Epsilon Constrained) due to it is increased with epsilon constrained method, for solving constrained (numerical) opti- mization problems. We validate our proposed approach with a set of 36 test functions provided for the CEC 2010 competition. We indirectly compare our results with respect to a version of the differential evolution algorithm. Our results show that TCEC can found feasible solutions on almost test functions with 10 and 30 decision variables.
publishDate 2012
dc.date.none.fl_str_mv 2012-10
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
info:eu-repo/semantics/publishedVersion
Objeto de conferencia
http://purl.org/coar/resource_type/c_5794
info:ar-repo/semantics/documentoDeConferencia
format conferenceObject
status_str publishedVersion
dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/23590
url http://sedici.unlp.edu.ar/handle/10915/23590
dc.language.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:SEDICI (UNLP)
instname:Universidad Nacional de La Plata
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reponame_str SEDICI (UNLP)
collection SEDICI (UNLP)
instname_str Universidad Nacional de La Plata
instacron_str UNLP
institution UNLP
repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
repository.mail.fl_str_mv alira@sedici.unlp.edu.ar
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