Johnson, Colin G. (2003) Artificial Immune Systems Programming for Symbolic Regression. In: Ryan, C. and Soule, T. and Keijzer, M. and Tsang, E. and Poli, R. and Costa, E., eds. Lecture Notes In Computer Science. LNCS 2610, 2610. Springer pp. 345-353. ISBN 3-540-00971-X.
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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:||19 Mar 2009 08:06|
|Resource URI:||http://kar.kent.ac.uk/id/eprint/13987 (The current URI for this page, for reference purposes)|
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