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Evolutionary Computation

Fall 2013, Vol. 21, No. 3, Pages 361-387
(doi: 10.1162/EVCO_a_00080)
© 2013 Massachusetts Institute of Technology
Dynamical Genetic Programming in XCSF
Article PDF (2.57 MB)
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.