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Título: Optimizing constrained problems through a T-Cell artificial immune system
Autores: Aragón, Victoria S.
Esquivel, Susana Cecilia
Coello Coello, Carlos
Fecha: 2009-04-13
2008
Publicador: Unversidad Nacional de La Plata
Fuente:


Tipo: Articulo
Articulo
Tema: artificial immune system; constrained optimization problem
Ciencias Informáticas
Organos artificiales
Algoritmos
Métodos de análisis estocástico
Descripción: In this paper, we present a new model of an artificial immune system (AIS), based on the process that suffers the T-Cell, it is called T-Cell Model. It 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-theart in the area), with respect to an AIS previously proposed and a self-organizing migrating genetic algorithm for constrained optimization (C-SOMGA)
Idioma: Inglés