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

How to teach Deep Blue to play poker and deliver groceries
http://bit.ly/1DXGeZD
If you are interested in hyper-heuristics, take a look at my publications in this area
http://bit.ly/efxLGg

Latest Blog Post

Snooker: Celebrating 40 years at the Crucible

Random Blog Post

Videos on the basics of Java

Publication(s)

A Simulated Annealing Enhancement of the Best-Fit Heuristic for the Orthogonal Stock-Cutting Problem
http://bit.ly/fsNXXk
An investigation of a hyperheuristic genetic algorithm applied to a trainer scheduling problem
http://bit.ly/egxt0d
Co-Evolving Neural networks with Evolutionary Strategies: A New Application to Divisia Money
http://bit.ly/eBV6pc
Repeated Goofspiel: A Game of Pure Strategy
http://bit.ly/1hWAFiz

Graham Kendall: Details of Requested Publication


Citation

Binner, J.M; Gazely, A.M and Kendall, G An evaluation of UK risky money: an artificial intelligence approach. Global Business and Economics Review, 11 (1): 1-18, 2009.


Abstract

In this paper we compare the performance of three indices in an inflation forecasting experiment. The evidence not only suggests that an evolved neural network is superior to traditionally trained networks in the majority of cases, but also that a risky money index performs at least as well as the Bank of England Divisia index when combined with interest rate informat1ion. Notably, the provision of long-term interest rates improves the out-of-sample forecasting performance of the Bank of England Divisia index in all cases examined.


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doi

The doi for this publication is 10.1504/GBER.2009.025378 You can link directly to the original paper, via the doi, from here

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Bibtex

@ARTICLE{bgk2009b, author = {J.M. Binner and A.M. Gazely and G. Kendall},
title = {An evaluation of UK risky money: an artificial intelligence approach},
journal = {Global Business and Economics Review},
year = {2009},
volume = {11},
pages = {1--18},
number = {1},
abstract = {In this paper we compare the performance of three indices in an inflation forecasting experiment. The evidence not only suggests that an evolved neural network is superior to traditionally trained networks in the majority of cases, but also that a risky money index performs at least as well as the Bank of England Divisia index when combined with interest rate informat1ion. Notably, the provision of long-term interest rates improves the out-of-sample forecasting performance of the Bank of England Divisia index in all cases examined.},
doi = {10.1504/GBER.2009.025378},
issn = {1097-4954},
keywords = {risky money index, artificial intelligence, inflation forecasting, neural networks, evolution strategies, Bank of England, Divisia index, interest rates},
owner = {est},
timestamp = {2010.02.22} }