Artificial Immune System for Solving Global Optimization Problems

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 global optimization problems. The model operates on four populations: Virgins, Effectors (CD4 and CD8) and Memory. Each of them has a different role, representation and procedures. We validate our proposed approach with a set of test functions taken from the specialized literature, we also compare our results with the results obtained by different bio-inspired approaches and we statistically analyze the results gotten by our approach.Fil: Aragon, Victoria Soledad. Universidad Nacional de San Luis. Facultad de Ciencias Físico Matemáticas y Naturales. Departamento de Informática. Laboratorio Investigación y Desarrollo En Inteligencia Computacional; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico San Luis; ArgentinaFil: Esquivel, Susana C.. 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 A.. CINVESTAV-IPN; Méxic

Artificial immune system for solving global optimization problems

En este trabajo, se presenta un nuevo modelo de Sistema Inmune Artificial (SIA) basado en los procesos que sufren las células T para resolver problemas de optimización global. El modelo, denominado MCT, trabaja sobre cuatro poblaciones con diferentes representaciones para las células y cada población atraviesa por distintos procesos. Se validó el modelo con 23 funciones tomadas de la literatura especializada. El modelo es comparado con diferentes enfoques bio-inspirados.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 global optimization problems. The model operates on four populations: Virgins, Effectors (CD4 and CD8) and Memory. Each of them has a different role, representation and procedures. We validate our proposed approach with a set of test functions taken from the specialized literature and we also compare our results with the results obtained by different bio-inspired approachesWorkshop de Agentes y Sistemas Inteligentes (WASI)Red de Universidades con Carreras en Informática (RedUNCI

Artificial immune system for solving global optimization problems

En este trabajo, se presenta un nuevo modelo de Sistema Inmune Artificial (SIA) basado en los procesos que sufren las células T para resolver problemas de optimización global. El modelo, denominado MCT, trabaja sobre cuatro poblaciones con diferentes representaciones para las células y cada población atraviesa por distintos procesos. Se validó el modelo con 23 funciones tomadas de la literatura especializada. El modelo es comparado con diferentes enfoques bio-inspirados.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 global optimization problems. The model operates on four populations: Virgins, Effectors (CD4 and CD8) and Memory. Each of them has a different role, representation and procedures. We validate our proposed approach with a set of test functions taken from the specialized literature and we also compare our results with the results obtained by different bio-inspired approachesWorkshop de Agentes y Sistemas Inteligentes (WASI)Red de Universidades con Carreras en Informática (RedUNCI

Hybridizing an immune artificial algorithm with simulated annealing for solving constrained optimization problems

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 model (TCSA) is increased with simulated annealing, for solving constrained (numerical) optimization problems. We validate our proposed approach with a set of test functions taken from the specialized literature. We indirectly compare our results with respect to GENOCOP III, a well known software based on genetic algorithmPresentado en el XII Workshop Agentes y Sistemas Inteligentes (WASI)Red de Universidades con Carreras en Informática (RedUNCI