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 the chair of the MISTA (Multidisciplinary International Conference on Scheduling: Theory and Applications)
http://bit.ly/hvZIaN
How to teach Deep Blue to play poker and deliver groceries
http://bit.ly/1DXGeZD

Latest Blog Post

Snooker: Celebrating 40 years at the Crucible

Random Blog Post

What is your Erdös Number?

Publication(s)

Repeated Goofspiel: A Game of Pure Strategy
http://bit.ly/1hWAFiz
Hybrid Heuristic for Multi-carrier Transportation Plans
http://bit.ly/1dGGwqO
Studying the Effect that a Linear Transformation has on the Time-Series Prediction Ability of an Evolutionary Neural Network
http://bit.ly/eyLaq2
A New Placement Heuristic for the Orthogonal Stock-Cutting Problem
http://bit.ly/gJdaAs

Graham Kendall: Details of Requested Publication


Citation

Nasreddine, H; Poh, H.S and Kendall, G Using an Evolutionary Algorithm for the Tuning of a Chess Evaluation Function Based on a Dynamic Boundary Strategy. In Proceedinsg of the 2006 IEEE Conference on Cybernetics and Intelligent Systems (CIS 2006), pages 1-6, 2006.


Abstract

One of the effective ways of optimising the evaluation function of a chess game is by tuning each of its parameters. Evolutionary algorithms have become an appropriate choice as optimisers. In the past works related to this domain, the values of the parameters are within a fixed boundary which means that no matter how the recombination and mutation operators are applied, the value of a given parameter cannot go beyond its corresponding interval. In this paper, we propose a new strategy called "dynamic boundary strategy" where the boundaries of the interval of each parameter are dynamic. A real-coded evolutionary algorithm that incorporates this strategy and uses the polynomial mutation as its main exploitative tool is implemented. The effectiveness of the proposed strategy is tested by competing our program against a popular commercial chess software. Our chess program has shown an autonomous improvement in performance after learning for hundreds of generations


pdf

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doi

The doi for this publication is 10.1109/ICCIS.2006.252366 You can link directly to the original paper, via the doi, from here

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Bibtex

@INPROCEEDINGS{npk2006, author = {H. Nasreddine and H.S. Poh and G. Kendall},
title = {Using an Evolutionary Algorithm for the Tuning of a Chess Evaluation Function Based on a Dynamic Boundary Strategy},
booktitle = {Proceedinsg of the 2006 IEEE Conference on Cybernetics and Intelligent Systems (CIS 2006)},
year = {2006},
pages = {1--6},
abstract = {One of the effective ways of optimising the evaluation function of a chess game is by tuning each of its parameters. Evolutionary algorithms have become an appropriate choice as optimisers. In the past works related to this domain, the values of the parameters are within a fixed boundary which means that no matter how the recombination and mutation operators are applied, the value of a given parameter cannot go beyond its corresponding interval. In this paper, we propose a new strategy called "dynamic boundary strategy" where the boundaries of the interval of each parameter are dynamic. A real-coded evolutionary algorithm that incorporates this strategy and uses the polynomial mutation as its main exploitative tool is implemented. The effectiveness of the proposed strategy is tested by competing our program against a popular commercial chess software. Our chess program has shown an autonomous improvement in performance after learning for hundreds of generations},
doi = {10.1109/ICCIS.2006.252366},
keywords = {chess, games, evolution, evolutionary algorithms},
owner = {gxk},
timestamp = {2011.01.01},
webpdf = {http://www.graham-kendall.com/papers/npk2006.pdf} }