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

Help solve Santa's logistics problems
http://bit.ly/1DXreuW
I am a member of the Automated Scheduling, Optimisation and Planning Research Group
http://bit.ly/eIQ5XC

Latest Blog Post

How Isaac Newton could help you beat the casino at roulette

Random Blog Post

Odds-setters as forecasters: The case of English Football

Publication(s)

An evaluation of UK risky money: an artificial intelligence approach
http://bit.ly/idh7zY
Providing a memory mechanism to enhance the evolutionary design of heuristics
http://bit.ly/fd4uYt
Fuzzy job shop scheduling with lot-sizing
http://bit.ly/gnd5ds
Towards the 'Decathlon 'Challenge' of search heuristics
http://bit.ly/edfHGs

Graham Kendall: Details of Requested Publication


Citation

Burke, E. K; Hyde, M. R and Kendall, G Grammatical Evolution of Local Search Heuristics. IEEE Transactions on Evolutionary Computation, 16 (3): 406-417, 2012.


Abstract

Genetic programming approaches have been employed in the literature to automatically design constructive heuristics for cutting and packing problems. These heuristics obtain results superior to human-created constructive heuristics, but they do not generally obtain results of the same quality as local search heuristics, which start from an initial solution and iteratively improve it. If local search heuristics can be successfully designed through evolution, in addition to a constructive heuristic which initializes the solution, then the quality of results which can be obtained by automatically generated algorithms can be significantly improved. This paper presents a grammatical evolution methodology which automatically designs good quality local search heuristics that maintain their performance on new problem instances.


pdf

You can download the pdf of this publication from here


doi

The doi for this publication is 10.1109/TEVC.2011.2160401 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 (3.654), 2013 (5.545), 2012 (4.810), 2011 (3.341), 2010 (4.403), 2009 (4.589), 2008 (3.736), 2007 (2.426), 2006 (3.770), 2005 (3.257), 2004 (3.688), 2003 (2.713), 2002 (1.486), 2001 (1.708)

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{bhk2012, author = {E. K. Burke and M. R. Hyde and G. Kendall},
title = {Grammatical Evolution of Local Search Heuristics},
journal = {IEEE Transactions on Evolutionary Computation},
year = {2012},
volume = {16},
pages = {406--417},
number = {3},
abstract = {Genetic programming approaches have been employed in the literature to automatically design constructive heuristics for cutting and packing problems. These heuristics obtain results superior to human-created constructive heuristics, but they do not generally obtain results of the same quality as local search heuristics, which start from an initial solution and iteratively improve it. If local search heuristics can be successfully designed through evolution, in addition to a constructive heuristic which initializes the solution, then the quality of results which can be obtained by automatically generated algorithms can be significantly improved. This paper presents a grammatical evolution methodology which automatically designs good quality local search heuristics that maintain their performance on new problem instances.},
doi = {10.1109/TEVC.2011.2160401},
issn = {1089-778X},
keywords = {Hyper-heuristics, hyperheuristics, Local Search, Grammatical Evolution, Bin Packing, Packing},
owner = {gxk},
timestamp = {2010.10.12},
webpdf = {http://www.graham-kendall.com/papers/bhk2012.pdf} }