Graham Kendall
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Professor Graham Kendall

Professor Graham Kendall is the Provost and CEO of The University of Nottingham Malaysia Campus (UNMC). He is also a Pro-Vice Chancellor of the University of Nottingham.

He is a Director of MyResearch Sdn Bhd, Crops for the Future Sdn Bhd. and Nottingham Green Technologies Sdn Bhd. He is a Fellow of the British Computer Society (FBCS) and a Fellow of the Operational Research Society (FORS).

He has published over 230 peer reviewed papers. He is an Associate Editor of 10 journals and the Editor-in-Chief of the IEEE Transactions of Computational Intelligence and AI in Games.

News

I am a member of the Automated Scheduling, Optimisation and Planning Research Group
http://bit.ly/eIQ5XC
How are football fixtures worked out?
http://bit.ly/1z0oTAH

Latest Blog Post

Snooker: Celebrating 40 years at the Crucible

Random Blog Post

Update: Displaying bibtex on web site

Publication(s)

Collective Behavior and Kin Selection in Evolutionary IPD
http://bit.ly/if34nF
Good Laboratory Practice for optimization research
http://bit.ly/1TFr8zD
A Hybrid Evolutionary Approach to the Nurse Rostering Problem
http://bit.ly/ey147Y
A Graph Coloring Constructive Hyper-Heuristic for Examination Timetabling Problems
http://bit.ly/1a3zv2M

Graham Kendall: Details of Requested Publication


Citation

Binner, J and Kendall, G Co-Evolving Neural networks with Evolutionary Strategies: A New Application to Divisia Money. In Proceedings of the International Conference on Artificial Intelligence 2002 (IC-AI'02), pages 884-889, CSREA Press, Monte Carlo Resort & Casino, Las Vegas, USA, 24-27 June, 2002.


Abstract

This work applies state-of-the-art artificial intelligence forecasting methods to provide new evidence of the comparative performance of statistically weighted Divisia indices vis a vis their simple sum counterparts in a simple inflation forecasting experiment. We develop a new approach that uses co-evolution (using neural networks and evolutionary strategies) as a predictive tool. This approach is simple to implement yet produces results that outperform stand-alone neural network predictions. Results suggest that superior tracking of inflation is possible for models that employ a Divisia M2 measure of money that has been adjusted to incorporate a learning mechanism to allow individuals to gradually alter their perceptions of the increased productivity of money. Divisia measures of money outperform their simple sum counterparts as macroeconomic indicators.


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Bibtex

@INPROCEEDINGS{bk2002, author = {J. Binner and G. Kendall},
title = {Co-Evolving Neural networks with Evolutionary Strategies: A New Application to Divisia Money},
booktitle = {Proceedings of the International Conference on Artificial Intelligence 2002 (IC-AI'02)},
year = {2002},
editor = {H.R. Arabnia and M. Youngsong},
pages = {884--889},
address = {Monte Carlo Resort \& Casino, Las Vegas, USA, 24-27 June},
publisher = {CSREA Press},
abstract = {This work applies state-of-the-art artificial intelligence forecasting methods to provide new evidence of the comparative performance of statistically weighted Divisia indices vis a vis their simple sum counterparts in a simple inflation forecasting experiment. We develop a new approach that uses co-evolution (using neural networks and evolutionary strategies) as a predictive tool. This approach is simple to implement yet produces results that outperform stand-alone neural network predictions. Results suggest that superior tracking of inflation is possible for models that employ a Divisia M2 measure of money that has been adjusted to incorporate a learning mechanism to allow individuals to gradually alter their perceptions of the increased productivity of money. Divisia measures of money outperform their simple sum counterparts as macroeconomic indicators.},
comment = {ISBN: 1-892512-27-0},
keywords = {neural networks, artificial neural networks, forecasting, divisa, evolution strategy, divisia},
timestamp = {2007.03.29},
webpdf = {http://www.graham-kendall.com/papers/bk2002.pdf} }