Created by W.Langdon from gp-bibliography.bib Revision:1.3989

@InProceedings{Preen:2011:ADGPitXLCS, title = "Arithmetic Dynamical Genetic Programming in the XCSF Learning Classifier System", author = "Richard J. Preen and Larry Bull", pages = "1427--1434", booktitle = "Proceedings of the 2011 IEEE Congress on Evolutionary Computation", year = "2011", editor = "Alice E. Smith", month = "5-8 " # jun, address = "New Orleans, USA", organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", ISBN = "0-7803-8515-2", keywords = "genetic algorithms, genetic programming, XCSF learning classifier system, arithmetic dynamical genetic programming, condition-action production system rules, continuous-valued dynamical system representation, nonlinear continuous-valued reinforcement learning problem, open-ended evolution, polynomial regression tasks, learning systems, pattern classification, polynomial approximation, regression analysis", DOI = "doi:10.1109/CEC.2011.5949783", abstract = "This paper presents results from an investigation into using a continuous-valued dynamical system representation within the XCSF Learning Classifier System. In particular, dynamical arithmetic genetic 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 dynamical systems within XCSF. The results presented herein show that the collective emergent behaviour of the evolved systems exhibits competitive performance with those previously reported on a non-linear continuous-valued reinforcement learning problem. In addition, the introduced system is shown to provide superior approximations to a number of composite polynomial regression tasks when compared with conventional tree-based genetic programming.", notes = "CEC2011 sponsored by the IEEE Computational Intelligence Society, and previously sponsored by the EPS and the IET.", }

Genetic Programming entries for Richard Preen Larry Bull