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An Overview of Artificial Immune Systems

Timmis, Jon and Knight, Thomas and de Castro, Leandro N. and Hart, Emma (2004) An Overview of Artificial Immune Systems. In: Paton, R. and Bolouri, H. and Holcombe, M. and Parish, J.H and Tateson, R., eds. Computation in Cells and Tissues: Perspectives and Tools for Thought. Natural Computation Series . Springer, pp. 51-86. ISBN 978-3-540-00358-8. (KAR id:14054)

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Abstract

The immune system is highly distributed, highly adaptive, self-organising in nature, maintains a memory of past encounters and has the ability to continually learn about new encounters. From a computational point of view, the immune system has much to offer by way of inspiration to computer scientists and engineers alike. As computational problems become more complex, increasingly, people are seeking out novel approaches to these problems, often turning to nature for inspiration. A great deal of attention is now being paid to the vertebrae immune system as a potential source of inspiration, where it is thought that different insights and alternative solutions can be gleaned, over and above other biologically inspired methods. Given this rise in attention to the immune system, it seems appropriate to explore this area in some detail. This survey explores the salient features of the immune system that are inspiring computer scientists and engineers to build Artificial Immune Systems (AIS). An extensive survey of applications is presented, ranging from network security to optimisation and machine learning. However, this is not complete, as no survey ever is, but it is hoped this will go some way to illustrate the potential of this exciting and novel area of research.

Item Type: Book section
Uncontrolled keywords: artificial immune systems, machine learning, review paper
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming,
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Mark Wheadon
Date Deposited: 24 Nov 2008 18:01 UTC
Last Modified: 16 Feb 2021 12:24 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/14054 (The current URI for this page, for reference purposes)
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