Discrete Dynamical Genetic Programming in XCS
Artificial Intelligence
2014-10-21 v2 Machine Learning
Neural and Evolutionary Computing
Systems and Control
Abstract
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.
Keywords
Cite
@article{arxiv.1204.4200,
title = {Discrete Dynamical Genetic Programming in XCS},
author = {Richard J. Preen and Larry Bull},
journal= {arXiv preprint arXiv:1204.4200},
year = {2014}
}
Comments
arXiv admin note: substantial text overlap with arXiv:1201.5604