versión On-line ISSN 1870-9044
Polibits no.43 México ene./jun. 2011
A Micro Artificial Immune System
Juan Carlos HerreraLozada1, Hiram Calvo1, and Hind Taud2
2 Centro de Innovación y Desarrollo Tecnológico en Cómputo, Instituto Politécnico Nacional, México D. F., 07700, Mexico (email: email@example.com).
Manuscript received March 12, 2011.
Manuscript accepted for publication June 6, 2011.
In this paper, we present a new algorithm, namely, a micro artificial immune system (MicroAIS) based on the Clonal Selection Theory for solving numerical optimization problems. For our study, we consider the algorithm CLONALG, a widely used artificial immune system. During the process of cloning, CLONALG greatly increases the size of its population. We propose a version with reduced population. Our hypothesis is that reducing the number of individuals in a population will decrease the number of evaluations of the objective function, increasing the speed of convergence and reducing the use of data memory. Our proposal uses a population of 5 individuals (antibodies), from which only 15 clones are obtained. In the maturation stage of the clones, two simple and fast mutation operators are used in a nominal convergence that works together with a reinitialization process to preserve the diversity. To validate our algorithm, we use a set of test functions taken from the specialized literature to compare our approach with the standard version of CLONALG. The same method can be applied in many other problems, for example, in text processing.
Key words: Artificial immune system, Clonal selection theory, micro algorithm, numerical optimization.
 D. Ashlock, Evolutionary Computation for Modeling and Optimization, Springer, 2005. [ Links ]
 M. Munetomo and Y. Satake, "Enhancing Modelbuilding Efficiency in Extended Compact Genetic Algorithms," in ICSMC '06. IEEE International Conference on Systems, Man and Cybernetics, 2006, Volume 3, Oct. 811, 2006, pp. 23622367. [ Links ]
 D. E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning, Addison Wesley, Reading, MA, 1989. [ Links ]
 L. Nunes de Castro and J. Timmis, Artificial Immune Systems: A new Computational Intelligence Approach, Springer, 2002. [ Links ]
 D. Dasgupta, "Advances in artificial immune systems," Computational Intelligence Magazine, IEEE, vol 1, issue 4, pp. 4049, Nov. 2006. [ Links ]
 A. Gelbukh, G. Sidorov, D. LaraReyes, and L. ChanonaHernandez, "Division of Spanish Words into Morphemes with a Genetic Algorithm," Lecture Notes in Computer Science, 5039, SpringerVerlag, pp. 1926, 2008. [ Links ]
 K. Krishnakumar, "Microgenetic algorithms for stationary and nonstationary function optimization" in SPIE Proceedings: Intelligent Control andAdaptive systems, 1989, pp. 289296. [ Links ]
 G. Alvarez, Can we make genetic algorithms work in highdimensionality problems? Stanford Exploration Project (SEP) report 112, 2002. [ Links ]
 G. Dozier, J. Bowen and D. Bahler, "Solving Small and Large Scale Constraint Satisfaction Problems Using a HeuristicBased Microgenetic Algorithm," in Proceedings of the First IEEE Conference on Evolutionary Computation (ICEC'94), Z. Michalewicz, J. D. Schaffer, H.P. Schwefel, D. B. Fogel and H. Kitano (eds), 1994, pp. 306311. [ Links ]
 G. Toscano, Carlos. A. Coello, "A MicroGenetic Algorithm for multiobjective optimization," in First International Conference on Evolutionary Multicriterion Optimization, Lecture Notes in Computer Science, vol. 1993, Springer, 2001, pp. 126140. [ Links ]
 Y. Ming and L. Cheng, "Application of Micro Genetic Algorithm to Optimization of TimeDomain UltraWide Band Antenna Array," in Microwave and Millimeter Wave Technology, 2007, ICMMT '07, International Conference, April 2007, pp. 14. [ Links ]
 J. Mendoza, D. Morales, R. López, J. Vannier, and C. A. Coello, "Multiobjective Location of Automatic Voltage Regulators in a Radial Distribution Network Using a Micro Genetic Algorithm," IEEE Transactions on Power Systems, vol. 22, issue 1, pp. 404412, Feb. 2007. [ Links ]
 J. C. Fuentes and C. A. Coello, "Handling Constraints in Particle Swarm Optimization Using a Small Population Size," Lecture Notes in Computer Science, MICAI 2007: Advances in Artificial Intelligence, vol. 4827, Springer, 2007. [ Links ]
 L. Nunes de Castro and F. J. Von Zuben, "The clonal selection algorithm with engineering applications," in Proceedings of Genetic and Evolutionary Computation Conference, Workshop on AISAA, July 2000, pp. 3637. [ Links ]
 L. Nunes de Castro and F. J. Von Zuben, "Learning and optimization using the clonal selection principle," IEEE Trans. Evol. Comput., vol. 6, no. 3, pp. 239251, Jun. 2002. [ Links ]
 E. Mezura, J. Velázquez, and C. A. Coello, "A comparative study of differential evolution variants for global optimization," in ACM, GECCO 2006, pp. 485492. [ Links ]