Timmis, Jon, Hone, Andrew N.W., Stibor, Thomas, Clark, Edward (2008) Theoretical advances in artificial immune systems. Theoretical Computer Science, 403 (1). pp. 11-32. ISSN 0304-3975. (doi:10.1016/j.tcs.2008.02.011) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:15264)
The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. | |
Official URL: http://dx.doi.org/10.1016/j.tcs.2008.02.011 |
Abstract
Artificial immune systems (AIS) constitute a relatively new area of bio-inspired computing. Biological models of the natural immune system, in particular the theories of clonal selection, immune networks and negative selection, have provided the inspiration Cor AIS algorithms. Moreover, such algorithms have been successfully employed in a wide variety of different application areas. However, despite these practical successes, until recently there has been a dearth of theory to justify their Use. In this paper, the existing theoretical work oil AIS is reviewed. After the presentation of a simple example of each of the three main types of AIS algorithm (that is, clonal selection, immune network and negative selection algorithms respectively), details of the theoretical analysis for each of these types are given. Some of the future challenges in this area are also highlighted.
Item Type: | Article |
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DOI/Identification number: | 10.1016/j.tcs.2008.02.011 |
Uncontrolled keywords: | artificial immune systems; clonal selection; negative selection; immune networks; Markov chains; k-CNF satisfiability |
Subjects: | Q Science > QA Mathematics (inc Computing science) > QA 75 Electronic computers. Computer science |
Divisions: | Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Mathematics, Statistics and Actuarial Science |
Depositing User: | Andrew Hone |
Date Deposited: | 12 May 2009 13:30 UTC |
Last Modified: | 05 Nov 2024 09:49 UTC |
Resource URI: | https://kar.kent.ac.uk/id/eprint/15264 (The current URI for this page, for reference purposes) |
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