title: Dynamical genetic programming in XCSF.
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.
authors.affiliation: Department of Computer Science and Creative Technologies, University of the West of England, Bristol, BS16 1QY, United Kingdom richard.preen@live.uwe.ac.uk
authors.forename: Richard J
authors.initials: RJ
authors.lastname: Preen
authors.forename: Larry
authors.initials: L
authors.lastname: Bull
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D008957:Models, Genetic; D008962:Models, Theoretical; D015999:Multivariate Analysis; D016571:Neural Networks, Computer; D016011:Normal Distribution; D011381:Programming Languages; D012044:Regression Analysis; D015203:Reproducibility of Results