Fuzzy Dynamical Genetic Programming in XCSF
Artificial Intelligence
2013-04-29 v1 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, and more recently Dynamical Genetic Programming (DGP). This paper presents results from an investigation into using a fuzzy DGP representation within the XCSF Learning Classifier System. In particular, asynchronous Fuzzy Logic 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 fuzzy dynamical systems within XCSF to solve several well-known continuous-valued test problems.
Cite
@article{arxiv.1204.4202,
title = {Fuzzy Dynamical Genetic Programming in XCSF},
author = {Richard J. Preen and Larry Bull},
journal= {arXiv preprint arXiv:1204.4202},
year = {2013}
}
Comments
2 page GECCO 2011 poster paper