International Journal of Computational Intelligence Research
  • Year: 2006
  • Volume: 2
  • Issue: 2

Optimised coverage of non-self with evolved lymphocytes in an Artificial Immune system

  • Author:
  • A.J. Graaff, A.P. Engelbrecht
  • Total Page Count: 24
  • Page Number: 127 to 150

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

*Email: agraaff@cs.up.ac.za

**Email: engel@cs.up.ac.za

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.

Keywords

artificial lymphocytes, non-self, negative selection, memory, classification