Dynamical Genetic Programming in XCSF

author = "Richard J. Preen and Larry Bull",

title = "Dynamical Genetic Programming in {XCSF}",

journal = "Evolutionary Computation",

year = "2013",

volume = "21",

number = "3",

pages = "361--387",

month = "Fall",

keywords = "genetic algorithms, genetic programming, Graph-based genetic programming, learning classifier systems, multistep-ahead prediction, reinforcement learning, self-adaptation, symbolic regression, XCSF, LCS",

ISSN = "1063-6560",

DOI = "

doi:10.1162/EVCO_a_00080",

URL = "

http://results.ref.ac.uk/Submissions/Output/502034",

size = "27 pages",

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.",

uk_research_excellence_2014 = "Knowledge representation and reasoning. This paper presents the first known approach to using the inherent dynamical behaviour of artficial genetic regulatory networks to predict the temporal dynamics of time series data.",