Juan Carlos Herrera–Lozada¹, Hiram Calvo¹, and Hind Taud²

¹ Centro de Investigación en Computación, Instituto Politécnico Nacional, México D. F., 07738, Mexico (e–mail: jlozada@ipn.mx, hcalvo@cic.ipn.mx).

² Centro de Innovación y Desarrollo Tecnológico en Cómputo, Instituto Politécnico Nacional, México D. F., 07700, Mexico (email: htaud@ipn.mx).

Manuscript received March 12, 2011.
Manuscript accepted for publication June 6, 2011.

Abstract

In this paper, we present a new algorithm, namely, a micro artificial immune system (Micro–AIS) 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.

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