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

Can ants play chess? Yes they can!
http://bit.ly/1yW3UhX
If you are interested in hyper-heuristics, take a look at my publications in this area
http://bit.ly/efxLGg

Latest Blog Post

How Isaac Newton could help you beat the casino at roulette

Random Blog Post

MISTA Conference: Plenary Talk (Raymond Kwan)

Publication(s)

An ant algorithm hyperheuristic for the project presentation scheduling problem
http://bit.ly/gbUor9
Document Zone Classification for Technical Document Images Using Artificial Neural Network and Support Vector Machines
http://bit.ly/1eUn8rs
Introducing Individual and Social Learning Into Evolutionary Checkers
http://bit.ly/1a2YwLx
Tabu Exponential Monte-Carlo with Counter Heuristic for Examination Timetabling.
http://bit.ly/fjry8k

Graham Kendall: Details of Requested Publication


Citation

Hyde, M; Burke, E. K and Kendall, G Evolving human-competitive reusable 2D strip packing heuristics. In Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference (GECCO 2009): Late Breaking Papers, pages 2189-2191, Montreal, Canada. 8-12 July, 2009.

Please note that this paper appeared in the Late Breaking Papers proceedings

A follow up journal paper (Burke, E. K; Hyde, M; Kendall, G and Woodward, J A Genetic Programming Hyper-Heuristic Approach for Evolving 2-D Strip Packing Heuristics. IEEE Transactions on Evolutionary Computation, 14 (6): 942-958, 2010) can be seen here


Abstract

This extended abstract presents preliminary work on reusable automatically generated heuristics for the 2D strip packing problem. It builds on our previous work, where the heuristics were not shown to be reusable. The best constructive heuristic for this problem in the literature is 'best-fit', and the motivation of this work is to obtain heuristics which are comparable to the performance of this heuristic.


pdf

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doi

The doi for this publication is 10.1145/1570256.1570299 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{hbk2009, author = {M. Hyde and E. K. Burke and G. Kendall},
title = {Evolving human-competitive reusable 2D strip packing heuristics},
booktitle = {Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference (GECCO 2009): Late Breaking Papers},
year = {2009},
pages = {2189--2191},
address = {Montreal, Canada. 8-12 July},
note = {Please note that this paper appeared in the Late Breaking Papers proceedings

A follow up journal paper (Burke, E. K; Hyde, M; Kendall, G and Woodward, J A Genetic Programming Hyper-Heuristic Approach for Evolving 2-D Strip Packing Heuristics. IEEE Transactions on Evolutionary Computation, 14 (6): 942-958, 2010) can be seen here},
abstract = {This extended abstract presents preliminary work on reusable automatically generated heuristics for the 2D strip packing problem. It builds on our previous work, where the heuristics were not shown to be reusable. The best constructive heuristic for this problem in the literature is 'best-fit', and the motivation of this work is to obtain heuristics which are comparable to the performance of this heuristic.},
doi = {10.1145/1570256.1570299},
keywords = {Genetic Programming, Hyper-heuristics, Cutting, Packing, hyperheuristics},
owner = {gxk},
timestamp = {2011.01.16} }