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Artificial Immune Systems

In: Handbook of Metaheuristics

Author

Listed:
  • Julie Greensmith

    (University of Nottingham)

  • Amanda Whitbrook

    (University of Nottingham)

  • Uwe Aickelin

    (University of Nottingham)

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.

Suggested Citation

  • Julie Greensmith & Amanda Whitbrook & Uwe Aickelin, 2010. "Artificial Immune Systems," International Series in Operations Research & Management Science, in: Michel Gendreau & Jean-Yves Potvin (ed.), Handbook of Metaheuristics, chapter 0, pages 421-448, Springer.
    • Handle: RePEc:spr:isochp:978-1-4419-1665-5_14
    DOI: 10.1007/978-1-4419-1665-5_14
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