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GECCO
2009
Springer

Discrete dynamical genetic programming in XCS

10 years 9 months ago
Discrete dynamical genetic programming in XCS
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 a discrete dynamical system representation within the XCS Learning Classifier System. In particular, asynchronous random Boolean networks are used to represent the traditional condition-action production system rules. It is shown possible to use self-adaptive, open-ended evolution to design an ensemble of such discrete dynamical systems within XCS to solve a number of well-known test problems. Categories and Subject Descriptors I.2.6 [Artificial Intelligence]: Learning – knowledge acquisition, parameter learning. General Terms Experimentation. Keywords Learning Classifier Systems, XCS, Random Boolean Networks, Reinforcement Learning, Self-Adaptation.
Richard Preen, Larry Bull
Added 26 May 2010
Updated 26 May 2010
Type Conference
Year 2009
Where GECCO
Authors Richard Preen, Larry Bull
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