Graham Kendall
Various Images

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

Can ants play chess? Yes they can!
http://bit.ly/1yW3UhX
I am involved with a spin out company that specialises in Strategic Resource Planning
http://bit.ly/eTPZO2

Latest Blog Post

Snooker: Celebrating 40 years at the Crucible

Random Blog Post

Improved Automatic Tweeting

Publication(s)

A Graph Coloring Constructive Hyper-Heuristic for Examination Timetabling Problems
http://bit.ly/1a3zv2M
An Evolutionary Methodology for the Automated Design of Cellular Automaton-based Complex Systems
http://bit.ly/hlJNZh
A Parameter-Free Hyperheuristic for Scheduling a Sales Summit
http://bit.ly/eb1k9A
EnHiC: An enforced hill climbing based system for general game playing
http://bit.ly/1VOTCY2

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

You can download the pdf of this publication from here


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

What is a doi?: A doi (Document Object Identifier) is a unique identifier for sicientific papers (and occasionally other material). This provides direct access to the location where the original article is published using the URL http://dx.doi/org/xxxx (replacing xxx with the doi). See http://dx.doi.org/ for more information



URL

This pubication does not have a URL associated with it.

The URL is only provided if there is additional information that might be useful. For example, where the entry is a book chapter, the URL might link to the book itself.


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} }