Dynamical genetic programming in XCSF

Preen, Richard J.; Preen, Richard; Bull, Larry

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.

• 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