Richard J. Preen
Dynamical genetic programming in XCSF
Preen, Richard J.; Preen, Richard; Bull, Larry
Authors
Richard Preen Richard2.Preen@uwe.ac.uk
Senior Research Fellow in Machine Learning
Lawrence Bull Larry.Bull@uwe.ac.uk
School Director (Research & Enterprise) and Professor
Abstract
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. © 2013 by the Massachusetts Institute of Technology.
Citation
Preen, R. J., Preen, R., & Bull, L. (2013). Dynamical genetic programming in XCSF. Evolutionary Computation, 21(3), 361-387. https://doi.org/10.1162/EVCO_a_00080
Journal Article Type | Article |
---|---|
Publication Date | Jan 1, 2013 |
Journal | Evolutionary Computation |
Print ISSN | 1063-6560 |
Electronic ISSN | 1530-9304 |
Publisher | Massachusetts Institute of Technology Press (MIT Press) |
Peer Reviewed | Peer Reviewed |
Volume | 21 |
Issue | 3 |
Pages | 361-387 |
DOI | https://doi.org/10.1162/EVCO_a_00080 |
Keywords | graph-based genetic programming, learning classifier systems, multistep-ahead prediction, reinforcement learning, self-adaptation, symbolic regression, XCSF |
Public URL | https://uwe-repository.worktribe.com/output/939917 |
Publisher URL | http://dx.doi.org/10.1162/EVCO_a_00080 |
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