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 are university examinations scheduled?
http://bit.ly/1z0pG4s
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

Parsing Bibtex Authors: How I Do It

Publication(s)

Iterated Local Search Using an Add and Delete Hyper-heuristic for University Course Timetabling
RATE_LIMIT_EXCEEDED
Evaluation of Two Dimensional Bin Packing Problem using the No Fit Polygon
RATE_LIMIT_EXCEEDED
Monte Carlo hyper-heuristics for examination timetabling
RATE_LIMIT_EXCEEDED
Measuring the Robustness of Airline Fleet Schedules
RATE_LIMIT_EXCEEDED

Graham Kendall: Details of Requested Publication


Citation

Kendall, G and Whitwell, G An Evolutionary Approach for the Tuning of a Chess Evaluation Function using Population Dynamics. In Proceedings of the 2001 Congress on Evolutionary Computation (CEC 2001), pages 995-1002, IEEE Press, 27-29 May, COEX Center, Seoul, Korea, 2001.


Abstract

Using the game of chess, this paper proposes an approach for the tuning of evaluation function parameters based on evolutionary algorithms. We introduce an iterative method for population member selection and show how the resulting win, loss, or draw information from competition can be used in conjunction with the statistical analysis of the population to develop evaluation function parameter values. A population of evaluation function candidates are randomly generated and exposed to the proposed learning techniques. An analysis to the success of learning is given and the undeveloped and developed players are examined through competition against a commercial chess program.


pdf

You can download the pdf of this publication from here


doi

The doi for this publication is 10.1109/CEC.2001.934299 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

The URL for additional information is http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=7440

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{kw2001a, author = {G. Kendall and G. Whitwell},
title = {An Evolutionary Approach for the Tuning of a Chess Evaluation Function using Population Dynamics},
booktitle = {Proceedings of the 2001 Congress on Evolutionary Computation (CEC 2001)},
year = {2001},
pages = {995--1002},
address = {27-29 May, COEX Center, Seoul, Korea},
publisher = {IEEE Press},
abstract = {Using the game of chess, this paper proposes an approach for the tuning of evaluation function parameters based on evolutionary algorithms. We introduce an iterative method for population member selection and show how the resulting win, loss, or draw information from competition can be used in conjunction with the statistical analysis of the population to develop evaluation function parameter values. A population of evaluation function candidates are randomly generated and exposed to the proposed learning techniques. An analysis to the success of learning is given and the undeveloped and developed players are examined through competition against a commercial chess program.},
comment = {ISBN 0-7803-6657-3},
doi = {10.1109/CEC.2001.934299},
keywords = {chess, games,, evolutionary computatioin, tuning, evaluation function},
timestamp = {2007.03.29},
url = {http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=7440},
webpdf = {http://www.graham-kendall.com/papers/kw2001a.pdf} }