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Altmetric Preen, R. and Bull, L. (2014) Discrete and fuzzy dynamical genetic programming in the XCSF learning classifier system. Soft Computing, 18 (1). pp. 153167. ISSN 14327643 Available from: http://eprints.uwe.ac.uk/22393 Full text not available from this repository Publisher's URL: http://dx.doi.org/10.1007/s0050001310444 Abstract/DescriptionA number of representation schemes have been presented for use within learning classifier systems, ranging from binary encodings to neural networks. This paper presents results from an investigation into using discrete and fuzzy dynamical system representations within the XCSF learning classifier system. In particular, asynchronous random Boolean networks are used to represent the traditional conditionaction production system rules in the discrete case and asynchronous fuzzy logic networks in the continuousvalued case. It is shown possible to use selfadaptive, openended evolution to design an ensemble of such dynamical systems within XCSF to solve a number of wellknown test problems.
