GA BASED SIMULATION OF IMMUNE NETWORKS APPLICATIONS IN STRUCTURAL OPTIMIZATION

Abstract

Genetic algorithms have received considerable recent attention in the optimal design of structural systems. These algorithms derive a computational leverage from an intrinsic pattern recognition capability, whereby patterns or schemata associated with a high level of fitness are identified and evolved 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 responsible for recognizing antigens foreign to the system and generating antibodies to combat the growth of these antigens. The paper describes key elements of how the functioning of the immune system can be modelled in the context of genetic search. It then provides an overview of the implications of this model in improving the convergence characteristics of genetic search, in particular, in the context of handling design constraints.