Dynamical genetic programming in XCSF

A number of representation schemes have been presented for use within learning classifier systems, ranging from binary encodings to artificial neural networks. This paper presents results from an investigation into using a temporally dynamic symbolic representation within the XCSF learning classifie...

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Veröffentlicht in:Evolutionary computation Jg. 21; H. 3; S. 361
Hauptverfasser: Preen, Richard J, Bull, Larry
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States 01.09.2013
Schlagworte:
Algorithms
Computational Biology - methods
Models, Genetic
Models, Theoretical
Multivariate Analysis
Neural Networks (Computer)
Normal Distribution
Programming Languages
Regression Analysis
Reproducibility of Results
ISSN:1530-9304, 1530-9304
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Zusammenfassung:A number of representation schemes have been presented for use within learning classifier systems, ranging from binary encodings to artificial neural networks. This paper presents results from an investigation into using a temporally dynamic symbolic representation within the XCSF learning classifier system. In particular, dynamical arithmetic networks are used to represent the traditional condition-action production system rules to solve continuous-valued reinforcement learning problems and to perform symbolic regression, finding competitive performance with traditional genetic programming on a number of composite polynomial tasks. In addition, the network outputs are later repeatedly sampled at varying temporal intervals to perform multistep-ahead predictions of a financial time series.
Bibliographie:ObjectType-Article-1
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ISSN:1530-9304
1530-9304
DOI:10.1162/EVCO_a_00080