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Arithmetic dynamical genetic programming in the XCSF learning classifier system

Preen, Richard; Bull, Larry

Authors

Richard Preen Richard2.Preen@uwe.ac.uk
Research Fellow - Deep Evolutionary Learning

Lawrence Bull Larry.Bull@uwe.ac.uk
AHOD Research and Scholarship and Prof



Abstract

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 ...

Presentation Conference Type Conference Paper (unpublished)
Start Date Jun 5, 2011
Publication Date Jan 1, 2011
Peer Reviewed Peer Reviewed
Pages 1428-1435
APA6 Citation Preen, R., & Bull, L. (2011, June). Arithmetic dynamical genetic programming in the XCSF learning classifier system. Paper presented at IEEE Congress on Evolutionary Computation (CEC), 2011
Keywords genetic algorithms, learning systems, pattern classification, polynominal approximation, regression analysis
Publisher URL http://dx.doi.org/10.1109/CEC.2011.5949783
Additional Information Title of Conference or Conference Proceedings : IEEE Congress on Evolutionary Computation (CEC), 2011