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

I have published a few papers on Sports Scheduling.
http://bit.ly/gVaUqT
I blog occasionally, feel free to take a look.
http://bit.ly/hq6rMK

Latest Blog Post

How Isaac Newton could help you beat the casino at roulette

Random Blog Post

3D Bin Packing, help Santa and share $10,000

Publication(s)

Irregular Packing using the Line and Arc No-Fit Polygon
http://bit.ly/hf6IdA
Journals Rankings: Buyer Beware
http://bit.ly/1iaSVYu
Ant Colonies Discover Knight's Tours
http://bit.ly/h0DqWF
Choice Function and Random HyperHeuristics
http://bit.ly/e7QYog

Graham Kendall: Details of Requested Publication


Citation

Burke, E. K; Hyde, M; Kendall, G and Woodward, J The Scalability of Evolved On Line Bin Packing Heuristics. In Proceedings of Congress on Evolutionary Computation (CEC 2007), pages 2530-2537, 2007.


Abstract

The on line bin packing problem concerns the packing of pieces into the least number of bins possible, as the pieces arrive in a sequential fashion. In previous work, we used genetic programming to evolve heuristics for this problem, which beat the human designed ‘best fit’ algorithm. Here we examine the performance of the evolved heuristics on larger instances of the problem, which contain many more pieces than the problem instances used in training. In previous work, we concluded that we could confidently apply our heuristics to new instances of the same class of problem. Now we can make the additional claim that we can confidently apply our heuristics to problems of much larger size, not only without deterioration of solution quality, but also within a constant factor of the performance obtained by ‘best fit’. Interestingly, our evolved heuristics respond to the number of pieces in a problem instance although they have no explicit access to that information. We also comment on the important point that, when solutions are explicitly constructed for single problem instances, the size of the search space explodes. However, when working in the space of algorithmic heuristics, the distribution of functions represented in the search space reaches some limiting distribution and therefore the combinatorial explosion can be controlled.


pdf

You can download the pdf of this publication from here


doi

The doi for this publication is 10.1109/CEC.2007.4424789 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{bhkw2007a, author = {E. K. Burke and M. Hyde and G. Kendall and J. Woodward},
title = {The Scalability of Evolved On Line Bin Packing Heuristics},
booktitle = {Proceedings of Congress on Evolutionary Computation (CEC 2007)},
year = {2007},
pages = {2530--2537},
month = {September 25-28},
organization = {Swissotel The Stamford, Singapore},
abstract = {The on line bin packing problem concerns the packing of pieces into the least number of bins possible, as the pieces arrive in a sequential fashion. In previous work, we used genetic programming to evolve heuristics for this problem, which beat the human designed ‘best fit’ algorithm. Here we examine the performance of the evolved heuristics on larger instances of the problem, which contain many more pieces than the problem instances used in training. In previous work, we concluded that we could confidently apply our heuristics to new instances of the same class of problem. Now we can make the additional claim that we can confidently apply our heuristics to problems of much larger size, not only without deterioration of solution quality, but also within a constant factor of the performance obtained by ‘best fit’. Interestingly, our evolved heuristics respond to the number of pieces in a problem instance although they have no explicit access to that information. We also comment on the important point that, when solutions are explicitly constructed for single problem instances, the size of the search space explodes. However, when working in the space of algorithmic heuristics, the distribution of functions represented in the search space reaches some limiting distribution and therefore the combinatorial explosion can be controlled.},
doi = {10.1109/CEC.2007.4424789},
keywords = {hyper-heuristics, hyperheuristics, meta-heuristics, metaheuristics, packing, bin packing, evolution, heuristics},
timestamp = {2007.06.26},
webpdf = {http://www.graham-kendall.com/papers/bhkw2007a.pdf} }