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 wriiten a number of articles for TheConversation
http://bit.ly/1yWlOkE
I have published a number of papers on Cutting and Packing
http://bit.ly/dQPw7T

Latest Blog Post

How Isaac Newton could help you beat the casino at roulette

Random Blog Post

2015 General Election Prediction: Wisdom of the Crowds

Publication(s)

Backward Induction and Repeated Games With Strategy Constraints: An Inspiration From the Surprise Exam Paradox
http://bit.ly/1ib50Nd
A Multiobjective Approach for UK Football Scheduling
http://bit.ly/fV4caa
An Evolutionary Approach for the Tuning of a Chess Evaluation Function using Population Dynamics
http://bit.ly/dFh029
When Sports Rules Go Awry
http://bit.ly/2ehzLjV

Graham Kendall: Details of Requested Publication


Citation

Cowling, P; Kendall, G and Han, L An investigation of a hyperheuristic genetic algorithm applied to a trainer scheduling problem. In Proceedings of the 2002 Congress on Evolutionary Computation (CEC 2002), pages 1185-1190, Hilton Hawaiian Village Hotel, Honolulu, Hawaii, 12-17 may, 2002.


Abstract

This paper investigates a genetic algorithm based hyperheuristic (hyper-GA) for scheduling geographically distributed training staff and courses. The aim of the hyper-GA is to evolve a good-quality heuristic for each given instance of the problem and use this to find a solution by applying a suitable ordering from a set of low-level heuristics. Since the user only supplies a number of low-level problem-specific heuristics and an evaluation function, the hyperheuristic can easily be reimplemented for a different type of problem, and we would expect it to be robust across a wide range of problem instances. We show that the problem can be solved successfully by a hyper-GA, presenting results for four versions of the hyper-GA as well as a range of simpler heuristics and applying them to five test data set.


pdf

You can download the pdf of this publication from here


doi

The doi for this publication is 10.1109/CEC.2002.1004411 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{ckh2002, author = {P. Cowling and G. Kendall and L. Han},
title = {An investigation of a hyperheuristic genetic algorithm applied to a trainer scheduling problem},
booktitle = {Proceedings of the 2002 Congress on Evolutionary Computation (CEC 2002)},
year = {2002},
pages = {1185--1190},
address = {Hilton Hawaiian Village Hotel, Honolulu, Hawaii, 12-17 may},
abstract = {This paper investigates a genetic algorithm based hyperheuristic (hyper-GA) for scheduling geographically distributed training staff and courses. The aim of the hyper-GA is to evolve a good-quality heuristic for each given instance of the problem and use this to find a solution by applying a suitable ordering from a set of low-level heuristics. Since the user only supplies a number of low-level problem-specific heuristics and an evaluation function, the hyperheuristic can easily be reimplemented for a different type of problem, and we would expect it to be robust across a wide range of problem instances. We show that the problem can be solved successfully by a hyper-GA, presenting results for four versions of the hyper-GA as well as a range of simpler heuristics and applying them to five test data set.},
comment = {ISBN 0-7803-7282-4},
doi = {10.1109/CEC.2002.1004411},
keywords = {hyper-heuristics, hyperheuristics, genetic algorithms, GA, scheduling, personnel rostering},
timestamp = {2007.03.29},
webpdf = {http://www.graham-kendall.com/papers/ckh2002.pdf} }