Immunology as a Metaphor for Computational ... - Napier University

Chapter 2. Background 24Library 1 Library 2 Library 3A1 A2 A3 A4 B1 B2 B3 B4 C1 C2 C3 C4A2 B1 C4A2 B1 C43 chosen segmentsA2 B1 C4one expressed antibodyFigure 2.6: Expressing an antibody from an artificial immune systeminteresting observation to emerge from this work is the fact that selection pressureacting only on the phenotype is capable of acting on the genotype. This suggests thatthe model might be extended to more complex fitness functions and applications inwhich it is straightforward to calculate some measure of phenotypic fitness of a singleantibody but more difficult to quantify the fitness of the entire immune system as awhole. So far however, the model appears only to have been used in studies of naturalimmunological processes, and not extended to other domains.2.2.2.2 Emergent Fitness SharingThe library based model just discussed uses one bit string to represent an entire immunesystem. An alternative approach is taken by [Smith et al., 1993] who propose apopulation based algorithm in which a genetic algorithm is used to produce and maintainmultiple subpopulations of antibodies within the complete population. Therefore,an immune system in this case is represented by the entire population manipulatedby the genetic algorithm, rather than a single individual in the population as in Hightower’swork. An important difference between Smith’s model and the library-basedmodel that arises from this is that in Smith’s model the bit string represents both thegenes that code for an antibody and the phenotypic expression of the antibody itself,and thus is a further simplification of the natural immune system.The algorithm proposed in [Smith et al., 1993] to evolve and maintain a diversepopulation of antibody niches is known as emergent fitness sharing.