Artificial immune systems

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...

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Main Authors: Greensmith, Julie, Whitbrook, Amanda, Aickelin, Uwe
Other Authors: Gendreau, Michel
Format: Book Section
Published: Springer 2010
Online Access:https://eprints.nottingham.ac.uk/1268/
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author Greensmith, Julie
Whitbrook, Amanda
Aickelin, Uwe
author2 Gendreau, Michel
author_facet Gendreau, Michel
Greensmith, Julie
Whitbrook, Amanda
Aickelin, Uwe
author_sort Greensmith, Julie
building Nottingham Research Data Repository
collection Online Access
description 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.
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spelling nottingham-12682020-05-04T20:25:31Z https://eprints.nottingham.ac.uk/1268/ Artificial immune systems Greensmith, Julie Whitbrook, Amanda Aickelin, Uwe 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. Springer Gendreau, Michel Potvin, Jean-Yves 2010 Book Section PeerReviewed Greensmith, Julie, Whitbrook, Amanda and Aickelin, Uwe (2010) Artificial immune systems. In: Handbook of metaheuristics. International series in operations research & management science, 2nd ed (146). Springer, Dordrecht, pp. 421-448. ISBN 9781441916631 http://www.springer.com/business+%26+management/operations+research/book/978-1-4419-1663-1
spellingShingle Greensmith, Julie
Whitbrook, Amanda
Aickelin, Uwe
Artificial immune systems
title Artificial immune systems
title_full Artificial immune systems
title_fullStr Artificial immune systems
title_full_unstemmed Artificial immune systems
title_short Artificial immune systems
title_sort artificial immune systems
url https://eprints.nottingham.ac.uk/1268/
https://eprints.nottingham.ac.uk/1268/