Constrained genetic search via schema adaptation: An immune network solution

Abstract

Genetic search derives its computational advantage from an intrinsic pattern recognition capability. Patterns or schemata associated with a high level of fitness are rapidly identified and reproduced at a near-exponential growth rate through generations of simulated evolution. This highly exploitative search process has been shown to be extremely effective in searching for schema that represent an optimum, requiring only that an appropriate measure of fitness be defined. This exploitative pattern recognition process is also at work in another biological system-the immune system which recognizes antigens foreign to the system and generates antibodies to combat the growth of these antigens. The present paper describes key elements of how the functioning of the immune system can be modeled in the context of genetic search, and its applicability for handling constrained genetic search. Results from this simulation are compared with those obtained from the more traditional approach of handling constraints in genetic search, viz. through the use of a penalty function formulation.