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 published a few papers on Sports Scheduling.
http://bit.ly/gVaUqT
How are football fixtures worked out?
http://bit.ly/1z0oTAH

Latest Blog Post

Snooker: Celebrating 40 years at the Crucible

Random Blog Post

PATAT 2010: Googlemaps and Multimaps

Publication(s)

Channel Assignment Optimisation Using a Hyper-Heuristic
http://bit.ly/eIazTf
On Nie-Tan operator and type-reduction of interval type-2 fuzzy sets
http://bit.ly/2kqxtD3
A Hyperheuristic Approach to Scheduling a Sales Summit
http://bit.ly/f5PRaP
Automatic Heuristic Generation with Genetic Programming: Evolving a Jack-of-all-Trades or a Master of One
http://bit.ly/faYgHK

Graham Kendall: Details of Requested Publication


Citation

Burke, E.K; Hyde, M; Kendall, G and Woodward, J Automating the Packing Heuristic Design Process with Genetic Programming. Evolutionary Computation, 20 (1): 63-89, 2012.


Abstract

The literature shows that one-, two-, and three-dimensional bin packing and knapsack packing are difficult problems in operational research. Many techniques, including exact, heuristic, and metaheuristic approaches, have been investigated to solve these problems and it is often not clear which method to use when presented with a new instance. This paper presents an approach which is motivated by the goal of building computer systems which can design heuristic methods. The overall aim is to explore the possibilities for automating the heuristic design process. We present a genetic programming system to automatically generate a good quality heuristic for each instance. It is not necessary to change the methodology depending on the problem type (one-, two-, or three-dimensional knapsack and bin packing problems), and it therefore has a level of generality unmatched by other systems in the literature. We carry out an extensive suite of experiments and compare with the best human designed heuristics in the literature. Note that our heuristic design methodology uses the same parameters for all the experiments. The contribution of this paper is to present a more general packing methodology than those currently available, and to show that, by using this methodology, it is possible for a computer system to design heuristics which are competitive with the human designed heuristics from the literature. This represents the first packing algorithm in the literature able to claim human competitive results in such a wide variety of packing domains.


pdf

You can download the pdf of this publication from here


doi

The doi for this publication is 10.1162/EVCO_a_00044 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


Journal Rankings


ISI Web of Knowledge Journal Citation Reports

The Web of Knowledge Journal Citation Reports (often known as ISI Impact Factors) help measure how often an article is cited. You can get an introduction to Journal Citation Reports here. Below I have provided the ISI impact factor for the jourrnal in which this article was published. For complete information I have shown the ISI ranking over a number of years, with the latest ranking highlighted.

2014 (2.366), 2013 (3.733), 2012 (2.109), 2011 (1.061), 2010 (2.630), 2009 (3.103), 2008 (3.000), 2007 (1.575), 2006 (1.325), 2005 (1.568), 2004 (3.206), 2003 (2.395)

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

@ARTICLE{bhkw2012, author = {E.K. Burke and M. Hyde and G. Kendall and J. Woodward},
title = {Automating the Packing Heuristic Design Process with Genetic Programming},
journal = {Evolutionary Computation},
year = {2012},
volume = {20},
pages = {63--89},
number = {1},
abstract = {The literature shows that one-, two-, and three-dimensional bin packing and knapsack packing are difficult problems in operational research. Many techniques, including exact, heuristic, and metaheuristic approaches, have been investigated to solve these problems and it is often not clear which method to use when presented with a new instance. This paper presents an approach which is motivated by the goal of building computer systems which can design heuristic methods. The overall aim is to explore the possibilities for automating the heuristic design process. We present a genetic programming system to automatically generate a good quality heuristic for each instance. It is not necessary to change the methodology depending on the problem type (one-, two-, or three-dimensional knapsack and bin packing problems), and it therefore has a level of generality unmatched by other systems in the literature. We carry out an extensive suite of experiments and compare with the best human designed heuristics in the literature. Note that our heuristic design methodology uses the same parameters for all the experiments. The contribution of this paper is to present a more general packing methodology than those currently available, and to show that, by using this methodology, it is possible for a computer system to design heuristics which are competitive with the human designed heuristics from the literature. This represents the first packing algorithm in the literature able to claim human competitive results in such a wide variety of packing domains.},
booktitle = {Proceedings of the 2010 IEEE Congress on Evolutionary Computation (CEC 2010)},
doi = {10.1162/EVCO_a_00044},
issn = {1063-6560},
keywords = {packing, bin packing, hyper-heuristic, hyperheuristics, genetic programming},
organization = {Barcelona, Spain},
owner = {gxk},
timestamp = {2010.12.10},
webpdf = {http://www.graham-kendall.com/papers/bhkw2012.pdf} }