Solving constrained optimization using a t-cell artificial immune system

Autores
Aragon, Victoria Soledad; Esquivel, Susana Cecilia; Coello Coello, Carlos
Año de publicación
2008
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
In this paper, we present a novel model of an artificial immune system (AIS), based on the process that suffers the T-Cell. The proposed model  is used for solving constrained (numerical)  optimization problems. The  model operates on three populations: Virgins, Effectors and  Memory.  Each of them has a different role. Also, the model dynamically adapts  the tolerance factor in order to improve the exploration capabilities of  the algorithm.  We also develop a new mutation operator which  incorporates knowledge of the problem. We validate our proposed  approach with a set of test functions taken from the specialized  literature and we compare our results with respect to Stochastic  Ranking (which is an approach representative of the state-of-the-art in  the area) and  with respect to an AIS previously proposed.
Fil: Aragon, Victoria Soledad. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Luis; Argentina. Universidad Nacional de San Luis. Facultad de Ciencias Físico Matemáticas y Naturales. Departamento de Informática. Laboratorio Investigación y Desarrollo en Inteligencia Computacional; Argentina
Fil: Esquivel, Susana Cecilia. Universidad Nacional de San Luis. Facultad de Ciencias Físico Matemáticas y Naturales. Departamento de Informática. Laboratorio Investigación y Desarrollo en Inteligencia Computacional; Argentina
Fil: Coello Coello, Carlos. Instituto Politécnico Nacional. Centro de Investigación y de Estudios Avanzados; México
Materia
ARTIFICIAL IMMUNE SYSTEMS
CONSTRAINED OPTIMIZATION PROBLEMS
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/159798
Acceder
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network_acronym_str CONICETDig
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network_name_str CONICET Digital (CONICET)
spelling Solving constrained optimization using a t-cell artificial immune systemAragon, Victoria SoledadEsquivel, Susana CeciliaCoello Coello, CarlosARTIFICIAL IMMUNE SYSTEMSCONSTRAINED OPTIMIZATION PROBLEMShttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1In this paper, we present a novel model of an artificial immune system (AIS), based on the process that suffers the T-Cell. The proposed model  is used for solving constrained (numerical)  optimization problems. The  model operates on three populations: Virgins, Effectors and  Memory.  Each of them has a different role. Also, the model dynamically adapts  the tolerance factor in order to improve the exploration capabilities of  the algorithm.  We also develop a new mutation operator which  incorporates knowledge of the problem. We validate our proposed  approach with a set of test functions taken from the specialized  literature and we compare our results with respect to Stochastic  Ranking (which is an approach representative of the state-of-the-art in  the area) and  with respect to an AIS previously proposed.Fil: Aragon, Victoria Soledad. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Luis; Argentina. Universidad Nacional de San Luis. Facultad de Ciencias Físico Matemáticas y Naturales. Departamento de Informática. Laboratorio Investigación y Desarrollo en Inteligencia Computacional; ArgentinaFil: Esquivel, Susana Cecilia. Universidad Nacional de San Luis. Facultad de Ciencias Físico Matemáticas y Naturales. Departamento de Informática. Laboratorio Investigación y Desarrollo en Inteligencia Computacional; ArgentinaFil: Coello Coello, Carlos. Instituto Politécnico Nacional. Centro de Investigación y de Estudios Avanzados; MéxicoAsociación Española para la Inteligencia Artificial2008-10info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/159798Aragon, Victoria Soledad; Esquivel, Susana Cecilia; Coello Coello, Carlos; Solving constrained optimization using a t-cell artificial immune system; Asociación Española para la Inteligencia Artificial; Inteligencia Artificial; 12; 40; 10-2008; 7-221137-36011988-3064CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/http://journal.iberamia.org/public/Vol.1-14.html#2008info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-29T12:32:29Zoai:ri.conicet.gov.ar:11336/159798instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-10-29 12:32:29.958CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Solving constrained optimization using a t-cell artificial immune system
title Solving constrained optimization using a t-cell artificial immune system
spellingShingle Solving constrained optimization using a t-cell artificial immune system
Aragon, Victoria Soledad
ARTIFICIAL IMMUNE SYSTEMS
CONSTRAINED OPTIMIZATION PROBLEMS
title_short Solving constrained optimization using a t-cell artificial immune system
title_full Solving constrained optimization using a t-cell artificial immune system
title_fullStr Solving constrained optimization using a t-cell artificial immune system
title_full_unstemmed Solving constrained optimization using a t-cell artificial immune system
title_sort Solving constrained optimization using a t-cell artificial immune system
dc.creator.none.fl_str_mv Aragon, Victoria Soledad
Esquivel, Susana Cecilia
Coello Coello, Carlos
author Aragon, Victoria Soledad
author_facet Aragon, Victoria Soledad
Esquivel, Susana Cecilia
Coello Coello, Carlos
author_role author
author2 Esquivel, Susana Cecilia
Coello Coello, Carlos
author2_role author
author
dc.subject.none.fl_str_mv ARTIFICIAL IMMUNE SYSTEMS
CONSTRAINED OPTIMIZATION PROBLEMS
topic ARTIFICIAL IMMUNE SYSTEMS
CONSTRAINED OPTIMIZATION PROBLEMS
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv In this paper, we present a novel model of an artificial immune system (AIS), based on the process that suffers the T-Cell. The proposed model  is used for solving constrained (numerical)  optimization problems. The  model operates on three populations: Virgins, Effectors and  Memory.  Each of them has a different role. Also, the model dynamically adapts  the tolerance factor in order to improve the exploration capabilities of  the algorithm.  We also develop a new mutation operator which  incorporates knowledge of the problem. We validate our proposed  approach with a set of test functions taken from the specialized  literature and we compare our results with respect to Stochastic  Ranking (which is an approach representative of the state-of-the-art in  the area) and  with respect to an AIS previously proposed.
Fil: Aragon, Victoria Soledad. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Luis; Argentina. Universidad Nacional de San Luis. Facultad de Ciencias Físico Matemáticas y Naturales. Departamento de Informática. Laboratorio Investigación y Desarrollo en Inteligencia Computacional; Argentina
Fil: Esquivel, Susana Cecilia. Universidad Nacional de San Luis. Facultad de Ciencias Físico Matemáticas y Naturales. Departamento de Informática. Laboratorio Investigación y Desarrollo en Inteligencia Computacional; Argentina
Fil: Coello Coello, Carlos. Instituto Politécnico Nacional. Centro de Investigación y de Estudios Avanzados; México
description In this paper, we present a novel model of an artificial immune system (AIS), based on the process that suffers the T-Cell. The proposed model  is used for solving constrained (numerical)  optimization problems. The  model operates on three populations: Virgins, Effectors and  Memory.  Each of them has a different role. Also, the model dynamically adapts  the tolerance factor in order to improve the exploration capabilities of  the algorithm.  We also develop a new mutation operator which  incorporates knowledge of the problem. We validate our proposed  approach with a set of test functions taken from the specialized  literature and we compare our results with respect to Stochastic  Ranking (which is an approach representative of the state-of-the-art in  the area) and  with respect to an AIS previously proposed.
publishDate 2008
dc.date.none.fl_str_mv 2008-10
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/159798
Aragon, Victoria Soledad; Esquivel, Susana Cecilia; Coello Coello, Carlos; Solving constrained optimization using a t-cell artificial immune system; Asociación Española para la Inteligencia Artificial; Inteligencia Artificial; 12; 40; 10-2008; 7-22
1137-3601
1988-3064
CONICET Digital
CONICET
url http://hdl.handle.net/11336/159798
identifier_str_mv Aragon, Victoria Soledad; Esquivel, Susana Cecilia; Coello Coello, Carlos; Solving constrained optimization using a t-cell artificial immune system; Asociación Española para la Inteligencia Artificial; Inteligencia Artificial; 12; 40; 10-2008; 7-22
1137-3601
1988-3064
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://journal.iberamia.org/public/Vol.1-14.html#2008
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Asociación Española para la Inteligencia Artificial
publisher.none.fl_str_mv Asociación Española para la Inteligencia Artificial
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
_version_ 1847427281309401088
score 13.10058

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