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Artificial Immune Systems

  • Julie Greensmith
  • Amanda Whitbrook
  • Uwe Aickelin
Chapter
Part of the International Series in Operations Research & Management Science book series (ISOR, volume 146)

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.

Keywords

Negative Selection Anomaly Detection Artificial Immune System Human Immune System Immune Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

This work is supported by the EPSRC (EP/D071976/1). The authors would like to thank Jon Timmis for his feedback and assistance.

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Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • Julie Greensmith
    • 1
  • Amanda Whitbrook
    • 2
  • Uwe Aickelin
    • 2
  1. 1.School of Computer ScienceUniversity of NottinghamNottinghamUK
  2. 2.University of NottinghamNottinghamUK

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