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
How are football fixtures worked out?
http://bit.ly/1z0oTAH

Latest Blog Post

Snooker: Celebrating 40 years at the Crucible

Random Blog Post

Informing publishers of my tweeting activities

Publication(s)

The Importance of Look-Ahead Depth in Evolutionary Checkers
http://bit.ly/1bh6fGH
A Simulated Annealing Enhancement of the Best-Fit Heuristic for the Orthogonal Stock-Cutting Problem
http://bit.ly/fsNXXk
Evaluating decision-making units under uncertainty using fuzzy multi-objective nonlinear programming
http://bit.ly/2k79ATE
A Genetic Programming Hyper-Heuristic Approach for Evolving 2-D Strip Packing Heuristics
http://bit.ly/grTvxk

Graham Kendall: Details of Requested Publication


Citation

Burke, E.K; Hyde, M.R; Kendall, G and Woodward, J Automatic Heuristic Generation with Genetic Programming: Evolving a Jack-of-all-Trades or a Master of One. In Proceedings of the 9th annual conference on Genetic and evolutionary computation (GECCO 20077, pages 1559-1565, London, UK, 2007.


Abstract

It is possible to argue that online bin packing heuristics should be evaluated by using metrics based on their performance over the set of all bin packing problems, such as the worst case or average case performance. However, this method of assessing a heuristic would only be relevant to a user who employs the heuristic over a set of problems which is actually representative of the set of all possible bin packing problems. On the other hand, a real world user will often only deal with packing problems that are representative of a particular sub-set. Their piece sizes will all belong to a particular distribution. The contribution of this paper is to show that a Genetic Programming system can automate the process of heuristic generation and produce heuristics that are human-competitive over a range of sets of problems, or which excel on a particular sub-set. We also show that the choice of training instances is vital in the area of automatic heuristic generation, due to the trade-off between the performance and generality of the heuristics generated and their applicability to new problems.


pdf

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doi

The doi for this publication is 10.1145/1276958.1277273 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

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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{bhkw2007b, author = {E.K. Burke and M.R. Hyde and G. Kendall and J. Woodward},
title = {Automatic Heuristic Generation with Genetic Programming: Evolving a Jack-of-all-Trades or a Master of One},
booktitle = {Proceedings of the 9th annual conference on Genetic and evolutionary computation (GECCO 20077},
year = {2007},
pages = {1559--1565},
address = {London, UK},
abstract = {It is possible to argue that online bin packing heuristics should be evaluated by using metrics based on their performance over the set of all bin packing problems, such as the worst case or average case performance. However, this method of assessing a heuristic would only be relevant to a user who employs the heuristic over a set of problems which is actually representative of the set of all possible bin packing problems. On the other hand, a real world user will often only deal with packing problems that are representative of a particular sub-set. Their piece sizes will all belong to a particular distribution. The contribution of this paper is to show that a Genetic Programming system can automate the process of heuristic generation and produce heuristics that are human-competitive over a range of sets of problems, or which excel on a particular sub-set. We also show that the choice of training instances is vital in the area of automatic heuristic generation, due to the trade-off between the performance and generality of the heuristics generated and their applicability to new problems.},
doi = {10.1145/1276958.1277273},
keywords = {hyper-heuristic, hyperheuristic, genetic programming, packing, bin packing, heuristic generation},
timestamp = {2007.06.11},
webpdf = {http://www.graham-kendall.com/papers/bhkw2007b.pdf} }