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Soft Computing
Erschienen in: Soft Computing 1/2014

01.01.2014 | Methodologies and Application

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

verfasst von: Richard J. Preen, Larry Bull

Erschienen in: Soft Computing | Ausgabe 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.
Vorheriger Artikel A novel L1/2 regularization shooting method for Cox’s proportional hazards model
Nächster Artikel A more general risk assessment methodology using a soft set-based ranking technique

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Metadaten
Titel
Discrete and fuzzy dynamical genetic programming in the XCSF learning classifier system
verfasst von
Richard J. Preen
Larry Bull
Publikationsdatum
01.01.2014
Verlag
Springer Berlin Heidelberg
Erschienen in
Soft Computing / Ausgabe 1/2014
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI
https://doi.org/10.1007/s00500-013-1044-4

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