Artificial Immune Systems (2010)

TL;DR

Artificial Immune Systems (AIS) are computational algorithms inspired by the immune system, exhibiting properties like robustness and fault tolerance, with evolving models applied to diverse problems such as anomaly detection and robotics.

Contribution

This paper provides an overview of AIS evolution, highlighting the diversity of algorithms and presenting case studies on the idiotypic network and Dendritic Cell Algorithm.

Findings

AIS have been successfully applied to various domains

Two generations of AIS models exist, with increasing complexity

Diversity in AIS algorithms reflects immune system complexity

Abstract

The human immune system has numerous properties that make it ripe for exploitation in the computational domain, such as robustness and fault tolerance, and many different algorithms, collectively termed Artificial Immune Systems (AIS), have been inspired by it. Two generations of AIS are currently in use, with the first generation relying on simplified immune models and the second generation utilising interdisciplinary collaboration to develop a deeper understanding of the immune system and hence produce more complex models. Both generations of algorithms have been successfully applied to a variety of problems, including anomaly detection, pattern recognition, optimisation and robotics. In this chapter an overview of AIS is presented, its evolution is discussed, and it is shown that the diversification of the field is linked to the diversity of the immune system itself, leading to a number of algorithms as opposed to one archetypal system. Two case studies are also presented to help provide insight into the mechanisms of AIS; these are the idiotypic network approach and the Dendritic Cell Algorithm.