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
Help solve Santa's logistics problems
http://bit.ly/1DXreuW

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Publication(s)

Investigation of an Adaptive Cribbage Player
http://bit.ly/eVPybN
Irregular Packing using the Line and Arc No-Fit Polygon
http://bit.ly/hf6IdA
An Evolutionary Approach for the Tuning of a Chess Evaluation Function using Population Dynamics
http://bit.ly/dFh029
Applying Ant Algorithms and the No Fit Polygon to the Nesting Problem
http://bit.ly/gkQlW3

Graham Kendall: Details of Requested Publication


Citation

Kendall, G; Soubeiga, E and Cowling, P Choice Function and Random HyperHeuristics. In Proceedings of the 4th Asia-Pacific Conference on Simulated Evolution And Learning (SEAL 2002), pages 667-671, Orchid Country Club, Singapore, November 18-22, 2002.


Abstract

A hyperheuristic is a high-level heuristic which adaptively controls the combination of several low-level knowledge poor heuristics so that while using only cheap and easy to implement low-level heuristics, we may achieve solution quality approaching that of an expensive knowledge rich approach. Hyperheuristics have been successfully applied by the authors to three real-world problems of personnel scheduling. In this paper, the low-level behaviour of the choice-function based hyperheuristic is investigated and compared with a range of other heuristics and hyperheuristics. We show that the choice-function hyperheuristic makes an effective and realistic combination of the lowlevel heuristics at hand. Furthermore the combination of the low-level heuristics is intelligently adapted to both the problem being solved and the region of the search space currently being explored.


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Bibtex

@INPROCEEDINGS{ksc2002, author = {G. Kendall and E. Soubeiga and P. Cowling},
title = {Choice Function and Random HyperHeuristics},
booktitle = {Proceedings of the 4th Asia-Pacific Conference on Simulated Evolution And Learning (SEAL 2002)},
year = {2002},
pages = {667--671},
address = {Orchid Country Club, Singapore, November 18-22},
abstract = {A hyperheuristic is a high-level heuristic which adaptively controls the combination of several low-level knowledge poor heuristics so that while using only cheap and easy to implement low-level heuristics, we may achieve solution quality approaching that of an expensive knowledge rich approach. Hyperheuristics have been successfully applied by the authors to three real-world problems of personnel scheduling. In this paper, the low-level behaviour of the choice-function based hyperheuristic is investigated and compared with a range of other heuristics and hyperheuristics. We show that the choice-function hyperheuristic makes an effective and realistic combination of the lowlevel heuristics at hand. Furthermore the combination of the low-level heuristics is intelligently adapted to both the problem being solved and the region of the search space currently being explored.},
keywords = {hyperheuristic, hyper-heuristic, choice function, adaptation},
timestamp = {2007.03.29},
webpdf = {http://www.graham-kendall.com/papers/ksc2002.pdf} }