Arithmetic dynamical genetic programming in the XCSF learning classifier system

Preen, R. and Bull, L. (2011) Arithmetic dynamical genetic programming in the XCSF learning classifier system. In: IEEE Congress on Evolutionary Computation (CEC), 2011, New Orleans, US, 5th - 8th June, 2011. US: UNSPECIFIED, pp. 1428-1435 Available from: http://eprints.uwe.ac.uk/20714

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Publisher's URL: http://dx.doi.org/10.1109/CEC.2011.5949783

Abstract/Description

This paper presents results from an investigation into using a continuous-valued dynamical system representation within the XCSF Learning Classifier System. In particular, dynamical arithmetic genetic 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 dynamical systems within XCSF. The results presented herein show that the collective emergent behaviour of the evolved systems exhibits competitive ...

Item Type:Conference or Workshop Item (Paper)
Uncontrolled Keywords:genetic algorithms, learning systems, pattern classification, polynominal approximation, regression analysis
Faculty/Department:Faculty of Environment and Technology > Department of Computer Science and Creative Technologies
ID Code:20714
Deposited By: M. Clarke
Deposited On:17 Jul 2013 10:18
Last Modified:15 Nov 2016 22:20

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