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 have published a few papers on Sports Scheduling.
http://bit.ly/gVaUqT
How to teach Deep Blue to play poker and deliver groceries
http://bit.ly/1DXGeZD

Latest Blog Post

How Isaac Newton could help you beat the casino at roulette

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Publication(s)

Chapter 4: Genetic Algorithms
http://bit.ly/1sYEs1Q
Scheduling English Football Fixtures: Consideration of Two Conflicting Objectives
http://bit.ly/1hWKuwV
Hybridising heuristics within an estimation distribution algorithm for examination timetabling
http://bit.ly/1Plbd56
A New Dynamic Point Specification Approach to Optimise Surface Mount Placement Machine in Printed Circuit Board Assembly
http://bit.ly/hWCyZA

Graham Kendall: Details of Requested Publication


Citation

Binner, J.M; Gazely, A.M and Kendall, G Evaluating the performance of a EuroDivisia index using artificial intelligence techniques. In Proceedings of the 8th International Conference on Information Systems (JCIS 2005), pages 871-874, 2005.

An extended version of this paper was published in the International Journal of Automation and Computing, 5 (1): 58-62, 2008


Abstract

We compare two methods in order to predict inflation rates in Europe. One method uses a standard back propagation neural network and the other uses an evolutionary approach, where the network weights and the network architecture is evolved. Results indicate that back propagation produces superior results. However, the evolving network still produces reasonable results with the advantage that the experimental set-up is minimal. Also of interest is the fact that the Divisia measure of money is superior as a predictive tool over simple sum.


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Bibtex

@INPROCEEDINGS{bgk2005, author = {J.M. Binner and A.M. Gazely and G. Kendall},
title = {Evaluating the performance of a EuroDivisia index using artificial intelligence techniques},
booktitle = {Proceedings of the 8th International Conference on Information Systems (JCIS 2005)},
year = {2005},
pages = {871--874},
note = {An extended version of this paper was published in the International Journal of Automation and Computing, 5 (1): 58-62, 2008},
abstract = {We compare two methods in order to predict inflation rates in Europe. One method uses a standard back propagation neural network and the other uses an evolutionary approach, where the network weights and the network architecture is evolved. Results indicate that back propagation produces superior results. However, the evolving network still produces reasonable results with the advantage that the experimental set-up is minimal. Also of interest is the fact that the Divisia measure of money is superior as a predictive tool over simple sum.},
keywords = {EuroDivisia, Divisia, Money, inflation, evolution strategies, neural networks},
owner = {gxk},
timestamp = {2011.01.02},
webpdf = {http://www.graham-kendall.com/papers/bgk2005.pdf} }