FUNCTION OPTIMIZATION BY THE IMMUNE METAPHOR

Abstract

The main goal of the immune system is to protect an organism against pathogens. To be able to recognize unknown (i.e. never seen) pathogens, the immune system applies a number of methods allowing to maintain sufficient diversity of its receptors. The most important methods are clonal selection and suppression of ineffective receptors. In effect the immune system admits maturation affinity property: during its functioning it continuously improves its ability to recognize new types of pathogens. This idea had found many interesting computer-oriented applications. In this paper a simple and easy to implement algorithm for multi-modal as well as non-stationary functions optimization is proposed. It is based on clonal selection and cells suppression mechanisms. Empirical results confirming its usability for uni-, multi-modal and non-stationary functions optimization are presented, and a review of other immunity based approaches is given.

Keywords

• artificial immune systems
• multi-modal optimization
• non-stationary functions optimization
• immune memory
• clonal selection
• affinity maturation