USING GENETIC ALGORITHMS TO STUDY THE EVOLUTION OF PARATOPES AND ANTIBODIES IN ARTIFICIAL IMMUNE SYSTEMS

Two binary-encoded models describing some aspects of the evolution in an artificial immune system have been proposed and analyzed. The first model has focused on the evolution of a paratope's population, 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 adapting to a given antigen molecules population. A genetic algorithm was used to form adaptive niching inspired on Coevolutionary Shared Niching strategy ideas taken from an economic model of monopolistic competition where “businessmen” locate themselves among geographically distributed “clients” so as to maximize their profit. Numerical experiments with the two models are presented.