Fuzzy dynamical genetic programming in XCSF

Preen, R. and Bull, L. (2011) Fuzzy dynamical genetic programming in XCSF. In: Krasnogor, N. and Lanzi, P. L., eds. (2011) Proceedings of the 13th annual conference companion on Genetic and evolutionary computation. ACM, pp. 167-168. ISBN 9781450306904 Available from: http://eprints.uwe.ac.uk/25835

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Publisher's URL: http://dx.doi.org/10.1145/2001858.2001952

Abstract/Description

A number of representation schemes have been presented for use within Learning Classifier Systems, ranging from binary encodings to Neural Networks, and more recently Dynamical Genetic Programming (DGP). This paper presents results from an investigation into using a fuzzy DGP representation within the XCSF Learning Classifier System. In particular, asynchronous Fuzzy Logic 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 fuzzy dynamical systems within XCSF to solve several well-known continuous-valued test problems.

Item Type:Book Section
Uncontrolled Keywords:fuzzy logic networks, learning classifier systems, reinforcement learning, self-adaptation, XCSF
Faculty/Department:Faculty of Environment and Technology > Department of Computer Science and Creative Technologies
ID Code:25835
Deposited By: Dr R. Preen
Deposited On:26 Jun 2015 13:01
Last Modified:14 Feb 2017 15:23

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