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. Lecture Notes In Computer Science. LNCS 2610, 2610. Springer pp. 345-353. ISBN 3-540-00971-X. (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)

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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: Conference or workshop item (UNSPECIFIED)
Uncontrolled keywords: artificial immune systems, genetic programming, automated programming
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming,
Divisions: Faculties > Science Technology and Medical Studies > School of Computing > Applied and Interdisciplinary Informatics Group
Depositing User: Mark Wheadon
Date Deposited: 24 Nov 2008 18:01
Last Modified: 30 Jun 2014 11:23
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