A Novel Model of Artificial Immune System for Solving Constrained Optimization Problems with Dynamic Tolerance Factor

  • Victoria S. Aragón
  • Susana C. Esquivel
  • Carlos A. Coello Coello
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4827)

Abstract

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.

Keywords

Feasible Solution Memory Cell Mutation Operator Constrain Optimization Problem Constraint Violation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Victoria S. Aragón
    • 1
  • Susana C. Esquivel
    • 1
  • Carlos A. Coello Coello
    • 2
  1. 1.Laboratorio de Investigación y Desarrollo en Inteligencia Computacional, Universidad Nacional de San Luis, Ejército de los Andes 950, (5700) San LuisArgentina
  2. 2.CINVESTAV-IPN (Evolutionary Computation Group), Departamento de Computación, Av. IPN No. 2508, Col. San Pedro Zacatenco, México D.F. 07300México

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