Population Diversity in an Immune System Model: Implications for Genetic Search

In typical applications, genetic algorithms (GAs) process populations of potential problem solutions to evolve a single population member that specifies an "optimized " solution. The majority of GA analysis has focused these optimization applications. In other applications (notably learning classifier systems and certain connectionist learning systems), a GA searches for a population of cooperative structures that jointly perform a computational task. This paper presents an analysis of this type of GA problem. The analysis considers a simplified geneticsbased machine learning system: a model of an immune system. In this model, a GA must discover a set of pattern-matching antibodies that effectively match a set of antigen patterns. Analysis shows how a GA can automatically evolve and sustain a diverse, cooperative population. The cooperation emerges as a natural part of the antigen-antibody matching procedure. This emergent effect is shown to be similar to fitness sharing, an explicit ..