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
Does AI have a place in the board room?
http://bit.ly/1DXreuW

Latest Blog Post

How Isaac Newton could help you beat the casino at roulette

Random Blog Post

AlphaGo: Computers and Game Playing: A Very Timely Lecture

Publication(s)

Choice Function and Random HyperHeuristics
http://bit.ly/e7QYog
Detecting change and dealing with uncertainty in imperfect evolutionary environments
http://bit.ly/1zSwgTX
Aircraft Landing Problem using Hybrid Differential Evolution and Simple Descent Algorithm
http://bit.ly/1kqnagr
Sampling of Unique Structures and Behaviours in Genetic Programming
http://bit.ly/ehhZrr

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

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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

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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{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} }