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Artificial Immune Systems Programming for Symbolic Regression

Johnson, Colin G. (2003) Artificial Immune Systems Programming for Symbolic Regression. In: Ryan, Conor and Soule, Terence and Keijzer, Maarten and Tsang, Edward and Poli, Riccardo, eds. Genetic Programming 6th European Conference. Lecture Notes in Computer Science . Springer, Berlin, Germany, pp. 345-353. ISBN 978-3-540-00971-9. E-ISBN 978-3-540-36599-0. (doi:10.1007/3-540-36599-0_32) (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:13987)

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. (Contact us about this Publication)
Official URL
http://dx.doi.org/10.1007/3-540-36599-0_32

Abstract

Artificial Immune Systems are computational algorithms which take their inspiration from the way in which natural immune systems learn to respond to attacks on an organism. This paper discusses how such a system can be used as an alternative to genetic algorithms as a way of exploring program-space in a system similar to genetic programming. Some experimental results are given for a symbolic regression problem. The paper ends with a discussion of future directions for the use of artificial immune systems in program induction.

Item Type: Book section
DOI/Identification number: 10.1007/3-540-36599-0_32
Uncontrolled keywords: artificial immune systems, genetic programming, automated programming
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/13987 (The current URI for this page, for reference purposes)
Johnson, Colin G.: https://orcid.org/0000-0002-9236-6581
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