Discrete dynamical genetic programming in XCS

Preen, R. and Bull, L. (2009) Discrete dynamical genetic programming in XCS. In: 11th Annual conference on Genetic and evolutionary computation (GECCO '09), Montreal, Canada, July 8-12, 2009. Montreal, Canada: UNSPECIFIED, pp. 1299-1306 Available from: http://eprints.uwe.ac.uk/19007

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Publisher's URL: http://dx.doi.org/10.1145/1569901.1570075


A number of representation schemes have been presented for use within Learning Classifier Systems, ranging from binary encodings to neural networks. This paper presents results from an investigation into using a discrete dynamical system representation within the XCS Learning Classifier System. In particular, asynchronous random Boolean networks are used to represent the traditional condition-action production system rules. It is shown possible to use self-adaptive, open-ended evolution to design an ensemble of such discrete dynamical systems within XCS to solve a number of well-known test problems.

Item Type:Conference or Workshop Item (Paper)
Uncontrolled Keywords:XCS, discrete dynamical genetic programming
Faculty/Department:Faculty of Environment and Technology > Department of Computer Science and Creative Technologies
ID Code:19007
Deposited By: D. Johnson
Deposited On:12 Mar 2013 15:50
Last Modified:15 Nov 2016 22:20

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