Dynamical genetic programming in XCSF

Preen, R. and Bull, L. (2013) Dynamical genetic programming in XCSF. Evolutionary Computation, 21 (3). pp. 361-387. ISSN 1063-6560 Available from: http://eprints.uwe.ac.uk/20736

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Publisher's URL: http://dx.doi.org/10.1162/EVCO_a_00080

Abstract/Description

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

Item Type:Article
Uncontrolled Keywords:graph-based genetic programming, learning classifier systems, multistep-ahead prediction, reinforcement learning, self-adaptation, symbolic regression, XCSF
Faculty/Department:Faculty of Environment and Technology > Department of Computer Science and Creative Technologies
ID Code:20736
Deposited By: M. Clarke
Deposited On:12 Jul 2013 13:40
Last Modified:15 Nov 2016 22:20

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