International Journal of Computational Intelligence Research (IJCIR)

Volume 2, Number 2 (2006)

 


Optimised coverage of non-self with evolved lymphocytes in 

an Artificial Immune system



A.J. Graaff, A.P. Engelbrecht

Department of Computer Science, Computational Intelligence Research Group (CIRG), School of Information Technology, University of Pretoria, Pretoria 0002, South Africa.

 

Abstract
The natural immune system (NIS) protects the body against unwanted foreign material (non-self cells) that could damage the body (self cells). The NIS can be modeled into an artificial immune system (AIS) to detect any non-self patterns in a non-biological environment. Detectors in the NIS can change from their initial mature status to memory status detectors or to annihilated status. A memory detector is a detector that frequently detects non-self cells and is a general detector for a subset of non-self cells. The NIS uses these memory detectors in a faster response to non-self cells. The purpose of this paper is to present the genetic artificial immune system (GAIS) which evolves these non-self detectors and determine their state using a life counter function. Only detectors with mature or memory status are used to detect non-self. Thus, the number of detectors is dynamically determined by the life counter function. In the paper GAIS is applied to different classification problems.

Key words
artificial lymphocytes, non-self, negative selection, memory, classification.

______________________________________________________________________________________
[UP]