Discrete and fuzzy dynamical genetic programming in the XCSF learning classifier system

Preen, R. and Bull, L. (2014) Discrete and fuzzy dynamical genetic programming in the XCSF learning classifier system. Soft Computing, 18 (1). pp. 153-167. ISSN 1432-7643 Available from: http://eprints.uwe.ac.uk/22393

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Publisher's URL: http://dx.doi.org/10.1007/s00500-013-1044-4

Abstract/Description

A 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 condition-action production system rules in the discrete case and asynchronous fuzzy logic networks in the continuous-valued case. It is shown possible to use self-adaptive, open-ended evolution to design an ensemble of such dynamical systems within XCSF to solve a number of well-known test problems.

Item Type:Article
Uncontrolled Keywords:fuzzy logic networks, learning classifier systems, memory, random boolean networks, reinforcement learning, self-adaptation, XCSF
Faculty/Department:Faculty of Environment and Technology > Department of Computer Science and Creative Technologies
ID Code:22393
Deposited By: Dr R. Preen
Deposited On:06 Feb 2014 13:58
Last Modified:15 Nov 2016 22:20

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