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

How are football fixtures worked out?
http://bit.ly/1z0oTAH
Help solve Santa's logistics problems
http://bit.ly/1DXreuW

Latest Blog Post

Snooker: Celebrating 40 years at the Crucible

Random Blog Post

How to get ants to solve a chess problem

Publication(s)

A Survey and Analysis of Diversity Measures in Genetic Programming
http://bit.ly/eA1Rvy
Automating the Packing Heuristic Design Process with Genetic Programming
http://bit.ly/19OfB8C
The effect of memory size on the evolutionary stability of strategies in iterated prisoner's dilemma
http://bit.ly/1HXMzXa
An Investigation of a Tabu Search Based Hyper-Heuristic for Examination Timetabling
http://bit.ly/1mlqRSh

Graham Kendall: Details of Requested Publication


Citation

Burke, E and Kendall, G Comparison of meta-heuristic algorithms for clustering rectangles. Computers and Industrial Engineering, 37 (1-2): 383-386, 1999.

Proceedings of the 24th international conference on computers and industrial engineering


Abstract

In this paper we consider a simplified version of the stock cutting (two-dimensional bin packing) problem. We compare three meta-heuristic algorithms (genetic algorithm (GA), tabu search (TS) and simulated annealing (SA)) when applied to this problem. The results show that tabu search and simulated annealing produce good quality results. This is not the case with the genetic algorithm. The problem, and its representation, is fully described along with key test results.


pdf

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doi

The doi for this publication is 10.1016/S0360-8352(99)00099-6 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 (1.783), 2013 (1.690), 2012 (1.561), 2011 (1.589), 2010 (1.543), 2009 (1.491), 2008 (1.057), 2007 (0.554), 2006 (0.650), 2005 (0.347), 2004 (0.632), 2003 (0.413), 2002 (0.270), 2001 (0.391), 2000 (0.128)

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

@ARTICLE{bk1999, author = {E. Burke and G. Kendall},
title = {Comparison of meta-heuristic algorithms for clustering rectangles},
journal = {Computers and Industrial Engineering},
year = {1999},
volume = {37},
pages = {383--386},
number = {1--2},
month = {October 1999},
note = {Proceedings of the 24th international conference on computers and industrial engineering},
abstract = {In this paper we consider a simplified version of the stock cutting (two-dimensional bin packing) problem. We compare three meta-heuristic algorithms (genetic algorithm (GA), tabu search (TS) and simulated annealing (SA)) when applied to this problem. The results show that tabu search and simulated annealing produce good quality results. This is not the case with the genetic algorithm. The problem, and its representation, is fully described along with key test results.},
doi = {10.1016/S0360-8352(99)00099-6},
issn = {0360-8352},
keywords = {Cutting, Packing, Genetic Algorithm, Optimisation, Simulated Annealing, Stock Cutting, Tabu Search, Two Dimensional Bin Packing},
webpdf = {http://www.graham-kendall.com/papers/bk1999.pdf} }