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 some papers on timetabling.
http://bit.ly/hSGAhZ
The hunt for MH370
http://bit.ly/1DXRLbu

Latest Blog Post

Snooker: Celebrating 40 years at the Crucible

Random Blog Post

How can academics use Twitter effectively?

Publication(s)

Evaluation of Two Dimensional Bin Packing Problem using the No Fit Polygon
http://bit.ly/dIcplc
A Parameter-Free Hyperheuristic for Scheduling a Sales Summit
http://bit.ly/eb1k9A
Comparison of meta-heuristic algorithms for clustering rectangles
http://bit.ly/eQQ0Kd
Computing Nash Equilibria and Evolutionarily Stable States of Evolutionary Games
http://bit.ly/24qGpHI

Graham Kendall: Details of Requested Publication


Citation

Mujtaba, H; Kendall, G; Baig, A.R and Özcan, E Detecting change and dealing with uncertainty in imperfect evolutionary environments. Information Sciences, 302: 33-49, 2015.

ISSN: 0020-0255


Abstract

Imperfection of information is a part of our daily life; however, it is usually ignored in learning based on evolutionary approaches. In this paper we develop an Imperfect Evolutionary System that provides an uncertain and chaotic imperfect environment that presents new challenges to its habitants. We then propose an intelligent methodology which is capable of learning in such environments. Detecting changes and adapting to the new environment is crucial to exploring the search space and exploiting any new opportunities that may arise. To deal with these uncertain and challenging environments, we propose a novel change detection strategy based on a Particle Swarm Optimization system which is hybridized with an Artificial Neural Network. This approach maintains a balance between exploitation and exploration during the search process. A comparison of approaches using different Particle Swarm Optimization algorithms show that the ability of our learning approach to detect changes and adapt as per the new demands of the environment is high.


pdf

You can download the pdf of this publication from here


doi

The doi for this publication is 10.1016/j.ins.2014.12.053 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 (4.038), 2013 (3.893), 2012 (3.643), 2011 (2.833), 2010 (2.836), 2009 (3.291), 2008 (3.095), 2007 (2.147), 2006 (1.003), 2005 (0.723), 2004 (0.540), 2003 (0.447), 2002 (0.361), 2001 (0.264), 2000 (0.322)

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{mkbo2015, author = {H. Mujtaba and G. Kendall and A.R. Baig and E. \"{O}zcan},
title = {Detecting change and dealing with uncertainty in imperfect evolutionary environments},
journal = {Information Sciences},
year = {2015},
volume = {302},
pages = {33--49},
note = {ISSN: 0020-0255},
abstract = {Imperfection of information is a part of our daily life; however, it is usually ignored in learning based on evolutionary approaches. In this paper we develop an Imperfect Evolutionary System that provides an uncertain and chaotic imperfect environment that presents new challenges to its habitants. We then propose an intelligent methodology which is capable of learning in such environments. Detecting changes and adapting to the new environment is crucial to exploring the search space and exploiting any new opportunities that may arise. To deal with these uncertain and challenging environments, we propose a novel change detection strategy based on a Particle Swarm Optimization system which is hybridized with an Artificial Neural Network. This approach maintains a balance between exploitation and exploration during the search process. A comparison of approaches using different Particle Swarm Optimization algorithms show that the ability of our learning approach to detect changes and adapt as per the new demands of the environment is high.},
doi = {10.1016/j.ins.2014.12.053},
issn = {0020-0255},
owner = {Graham},
timestamp = {2014.12.03},
webpdf = {http://www.graham-kendall.com/papers/mkbo2015.pdf} }