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Artificial immune systems

Greensmith, Julie; Whitbrook, Amanda; Aickelin, Uwe


Amanda Whitbrook

Uwe Aickelin


Michel Gendreau

Jean-Yves Potvin


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.


Greensmith, J., Whitbrook, A., & Aickelin, U. (2010). Artificial immune systems. In M. Gendreau, & J. Potvin (Eds.), Handbook of metaheuristics. Springer

Publication Date Jan 1, 2010
Deposit Date Aug 18, 2011
Publicly Available Date Aug 18, 2011
Peer Reviewed Peer Reviewed
Volume 2nd ed
Issue 146
Series Title International series in operations research & management science
Book Title Handbook of metaheuristics
ISBN 9781441916631
Public URL
Publisher URL


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