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

If you are interested in hyper-heuristics, take a look at my publications in this area
http://bit.ly/efxLGg
I have wriiten a number of articles for TheConversation
http://bit.ly/1yWlOkE

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Publication(s)

Investigating the Use of Local Search for Improving Meta-Hyper-Heuristic Performance
http://bit.ly/1csb3rG
Detecting change and dealing with uncertainty in imperfect evolutionary environments
http://bit.ly/1zSwgTX
Introducing Individual and Social Learning Into Evolutionary Checkers
http://bit.ly/1a2YwLx
Evaluating the performance of a EuroDivisia index using artificial intelligence techniques
http://bit.ly/gaswDm

Graham Kendall: Details of Requested Publication


Citation

Bai, R; Burke, E. K; Kendall, G; Li, J and McCollum, B A Hybrid Evolutionary Approach to the Nurse Rostering Problem. IEEE Transactions on Evolutionary Computation, 14 (4): 580-590, 2010.


Abstract

Nurse rostering is an important search problem with many constraints. In the literature, a number of approaches have been investigated including penalty function methods to tackle these constraints within genetic algorithm frameworks. In this paper, we investigate an extension of a previously proposed stochastic ranking method, which has demonstrated superior performance to other constraint handling techniques when tested against a set of constrained optimization benchmark problems. An initial experiment on nurse rostering problems demonstrates that the stochastic ranking method is better at finding feasible solutions, but fails to obtain good results with regard to the objective function. To improve the performance of the algorithm, we hybridize it with a recently proposed simulated annealing hyper-heuristic (SAHH) within a local search and genetic algorithm framework. Computational results show that the hybrid algorithm performs better than both the genetic algorithm with stochastic ranking and the SAHH alone. The hybrid algorithm also outperforms the methods in the literature which have the previously best known results.


pdf

You can download the pdf of this publication from here


doi

The doi for this publication is 10.1109/TEVC.2009.2033583 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

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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{bbklm2010, author = {R. Bai and E. K. Burke and G. Kendall and J. Li and B. McCollum},
title = {A Hybrid Evolutionary Approach to the Nurse Rostering Problem},
journal = {IEEE Transactions on Evolutionary Computation},
year = {2010},
volume = {14},
pages = {580--590},
number = {4},
month = {July 2010},
abstract = {Nurse rostering is an important search problem with many constraints. In the literature, a number of approaches have been investigated including penalty function methods to tackle these constraints within genetic algorithm frameworks. In this paper, we investigate an extension of a previously proposed stochastic ranking method, which has demonstrated superior performance to other constraint handling techniques when tested against a set of constrained optimization benchmark problems. An initial experiment on nurse rostering problems demonstrates that the stochastic ranking method is better at finding feasible solutions, but fails to obtain good results with regard to the objective function. To improve the performance of the algorithm, we hybridize it with a recently proposed simulated annealing hyper-heuristic (SAHH) within a local search and genetic algorithm framework. Computational results show that the hybrid algorithm performs better than both the genetic algorithm with stochastic ranking and the SAHH alone. The hybrid algorithm also outperforms the methods in the literature which have the previously best known results.},
doi = {10.1109/TEVC.2009.2033583},
issn = {1089-778X},
keywords = {Nurse Rostering, Personnel Rostering, Genetic Algorithms, Simulated Annealing, Hyper-heuristics, Meta-heuristics, hyperheuristics, metaheuristics},
owner = {gxk},
timestamp = {2010.10.12},
webpdf = {http://www.graham-kendall.com/papers/bbklm2010.pdf} }