Hybridizing an immune artificial algorithm with epsilon constrained method

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,...

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Detalles Bibliográficos
Autores principales:	Aragón, Victoria S., Esquivel, Susana Cecilia
Formato:	Objeto de conferencia
Lenguaje:	Inglés
Publicado:	2012
Materias:	Ciencias Informáticas Algorithms Hybrid systems Optimization Intelligent agents Artificial Immune System Constrained Optimization Problem Epsilon Constrained Method
Acceso en línea:	http://sedici.unlp.edu.ar/handle/10915/23590
Aporte de:	SEDICI (UNLP) de Universidad Nacional de La Plata

id	I19-R120-10915-23590
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 Algorithms Hybrid systems Optimization Intelligent agents Artificial Immune System Constrained Optimization Problem Epsilon Constrained Method
spellingShingle	Ciencias Informáticas Algorithms Hybrid systems Optimization Intelligent agents Artificial Immune System Constrained Optimization Problem Epsilon Constrained Method Aragón, Victoria S. Esquivel, Susana Cecilia Hybridizing an immune artificial algorithm with epsilon constrained method
topic_facet	Ciencias Informáticas Algorithms Hybrid systems Optimization Intelligent agents Artificial Immune System Constrained Optimization Problem Epsilon Constrained Method
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.
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	Hybridizing an immune artificial algorithm with 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
publishDate	2012
url	http://sedici.unlp.edu.ar/handle/10915/23590
work_keys_str_mv	AT aragonvictorias hybridizinganimmuneartificialalgorithmwithepsilonconstrainedmethod AT esquivelsusanacecilia hybridizinganimmuneartificialalgorithmwithepsilonconstrainedmethod
bdutipo_str	Repositorios
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Hybridizing an immune artificial algorithm with epsilon constrained method

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