Solving constrained optimization using a T-Cell artificial immune system

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...

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Detalles Bibliográficos
Autores principales: Aragón, Victoria S., Esquivel, Susana Cecilia
Formato: Objeto de conferencia
Lenguaje:Inglés
Publicado: 2007
Materias:
Ciencias Informáticas
Informática
Intelligent agents
Constrained optimization
sistema inmune artificial
ARTIFICIAL INTELLIGENCE
problemas de optimización restringidos
artificial immune system
constrained optimization problem
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/23087
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-23087
record_format dspace
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Inglés
topic Ciencias Informáticas
Informática
Intelligent agents
Constrained optimization
sistema inmune artificial
ARTIFICIAL INTELLIGENCE
problemas de optimización restringidos
artificial immune system
constrained optimization problem
spellingShingle Ciencias Informáticas
Informática
Intelligent agents
Constrained optimization
sistema inmune artificial
ARTIFICIAL INTELLIGENCE
problemas de optimización restringidos
artificial immune system
constrained optimization problem
Aragón, Victoria S.
Esquivel, Susana Cecilia
Solving constrained optimization using a T-Cell artificial immune system
topic_facet Ciencias Informáticas
Informática
Intelligent agents
Constrained optimization
sistema inmune artificial
ARTIFICIAL INTELLIGENCE
problemas de optimización restringidos
artificial immune system
constrained optimization problem
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.
format Objeto de conferencia
Objeto de conferencia
author Aragón, Victoria S.
Esquivel, Susana Cecilia
author_facet Aragón, Victoria S.
Esquivel, Susana Cecilia
author_sort Aragón, Victoria S.
title Solving constrained optimization using a T-Cell artificial immune system
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
publishDate 2007
url http://sedici.unlp.edu.ar/handle/10915/23087
work_keys_str_mv AT aragonvictorias solvingconstrainedoptimizationusingatcellartificialimmunesystem
AT esquivelsusanacecilia solvingconstrainedoptimizationusingatcellartificialimmunesystem
bdutipo_str Repositorios
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