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Soft Computing
Published in: Soft Computing 1/2014

01-01-2014 | Methodologies and Application

Discrete and fuzzy dynamical genetic programming in the XCSF learning classifier system

Authors: Richard J. Preen, Larry Bull

Published in: Soft Computing | Issue 1/2014

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Abstract

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 discrete and fuzzy dynamical system representations within the XCSF learning classifier system. In particular, asynchronous random Boolean networks are used to represent the traditional condition-action production system rules in the discrete case and asynchronous fuzzy logic networks in the continuous-valued case. It is shown possible to use self-adaptive, open-ended evolution to design an ensemble of such dynamical systems within XCSF to solve a number of well-known test problems.
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Metadata
Title
Discrete and fuzzy dynamical genetic programming in the XCSF learning classifier system
Authors
Richard J. Preen
Larry Bull
Publication date
01-01-2014
Publisher
Springer Berlin Heidelberg
Published in
Soft Computing / Issue 1/2014
Print ISSN: 1432-7643
Electronic ISSN: 1433-7479
DOI
https://doi.org/10.1007/s00500-013-1044-4

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