RESEARCH PAPER Evolutionary algorithms to simulate the phylogenesis of a binary artificial immune system Grazziela P. Figueredo Luis A. V. de Carvalho Helio J. C. Barbosa Nelson F. F. Ebecken Received: 12 November 2007 / Revised: 12 March 2008 / Accepted: 13 March 2008 / Published online: 29 April 2008 Ó Springer-Verlag 2008 Abstract Four binary-encoded models describing some aspects of the phylogenetics evolution in an artificial immune system have been proposed and analyzed. The first model has focused on the evolution of a paratope’s popu- lation, considering a fixed group of epitopes, to simulate a hypermutation mechanism and observe how the system would self-adjust to cover the epitopes. In the second model, the evolution involves a group of antibodies adapt- ing to a given antigenic molecules’ population. The third model simulated the coevolution between antibodies’ gen- erating gene libraries and antigens. The objective was to simulate somatic recombination mechanisms to obtain final libraries apt to produce antibodies to cover any possible antigen that would appear in the pathogens’ population. In the fourth model, the coevolution involves a new population of self-molecules whose function was to establish restric- tions in the evolution of libraries’ population. For all the models implemented, evolutionary algorithms (EA) were used to form adaptive niching inspired in the coevolutionary shared niching strategy ideas taken from a monopolistic competition economic model where ‘‘businessmen’’ locate themselves among geographically distributed ‘‘clients’’ so as to maximize their profit. Numerical experiments and conclusions are shown. These considerations present many similarities to biological immune systems and also some inspirations to solve real-world problems, such as pattern recognition and knowledge discovery in databases. Keywords Artificial immune systems  Evolutionary computation  Artificial immune systems models 1 Introduction The immune system (IS) is able to protect us from a number of pathogens. It also monitors the organism, searching and destroying anomalous cells. To perform such tasks, the IS must recognize a great variety of different compounds and distinguish, among them, those which can remain in the organism and those that are to be eliminated. It is believed that the IS identifies about 10 16 foreign molecules [18], which means that it can identify any molecule [10]. The IS pattern recognition task is performed through surface receptor molecules of T and B cells. The identifi- cation of antigens in both these types of lymphocytes occurs differently. B cells recognize antigens through immune globulins from its cell surface. T cells recognize only antigens presented by an antigen presenting cell (APC). The creation of these receptors and their capability to cover all antigens have their origin in a very sophisti- cated genetic mechanism. During the receptor’s formation process, the variation is caused by the combinatorial associations among the receptors codifying genes and the hypermutation mechanism. The hypermutations occur in the lymph nodes’ germi- native centers. Thus, when an APC penetrates the lymph G. P. Figueredo (&)  L. A. V. de Carvalho  N. F. F. Ebecken Federal University of Rio de Janeiro - COPPE, Rio de Janeiro, Brazil e-mail: gpfigueredo@gmail.com L. A. V. de Carvalho e-mail: meucorreioeletronico@gmail.com N. F. F. Ebecken e-mail: nelson@ntt.ufrj.br H. J. C. Barbosa LNCC, MCT, Petro ´polis, Brazil e-mail: hcbm@lncc.br 123 Evol. Intel. (2008) 1:133–144 DOI 10.1007/s12065-008-0010-z