Optimizing constrained problems through a T-Cell artificial immune system


  • Victoria S. Aragón Laboratorio de Investigación y Desarrollo en Inteligencia Computacional, Universidad Nacional de San Luis, San Luis, Argentina
  • Susana Cecilia Esquivel Laboratorio de Investigación y Desarrollo en Inteligencia Computacional, Universidad Nacional de San Luis, San Luis, Argentina
  • Carlos Coello Coello CINVESTAV-IPN (Evolutionary Computation Group), Electrical Eng. Department, Computer Science Dept., México D.F., México


artificial immune system, constrained optimization problem


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).


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How to Cite

Aragón, V. S., Esquivel, S. C., & Coello Coello, C. (2008). Optimizing constrained problems through a T-Cell artificial immune system. Journal of Computer Science and Technology, 8(03), p. 158–165. Retrieved from https://journal.info.unlp.edu.ar/JCST/article/view/758



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