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

Can ants play chess? Yes they can!
http://bit.ly/1yW3UhX
I blog occasionally, feel free to take a look.
http://bit.ly/hq6rMK

Latest Blog Post

Snooker: Celebrating 40 years at the Crucible

Random Blog Post

Model Formulation: Vehicle Routing Problem (VRP)

Publication(s)

Using Harmony Search with Multiple Pitch Adjustment Operators for the Portfolio Selection Problem
http://bit.ly/1okj5eC
Using tree search bounds to enhance a genetic algorithm approach to two rectangle packing problems
http://bit.ly/gIBeuh
A Squeaky Wheel Optimisation Methodology for Two Dimensional Strip Packing
http://bit.ly/ibMskY
Tabu Exponential Monte-Carlo with Counter Heuristic for Examination Timetabling.
http://bit.ly/fjry8k

Graham Kendall: Details of Requested Publication


Citation

Terrazas, G; Siepmann, P; Kendall, G and Krasnogor, N An Evolutionary Methodology for the Automated Design of Cellular Automaton-based Complex Systems. Journal of Cellular Automata, 2 (1): 77-102, 2007.


Abstract

Cellular automata (CA) are an important modelling paradigm in the natural sciences and an extremely useful approach in the study of complex systems. Homogeneity, massive parallelism, local cellular interactions and both synchronous and asynchronous models of rule execution are some of their most prominent features, allowing scientists to model and understand a variety of phenomena in, to name but a few, the physical, chemical, biological, social and information sciences. An ubiquitous problem related with the study of complex systems by means of CA is that of parameter identification. In some cases, analytical methods are available but in many others, due to the bottom-up complexity of the underlying processes, the best route for CA identification is through design optimization by means of a metaheuristic, such as an evolutionary algorithm. In this work we report on a systematic methodology we have developed to control the spatio-temporal behavior of a CA in order to obtain a ‘designoid’ target pattern. Four independent CA-based complex systems were used to assess our hypothesis which combines clustering, fitness distance correlation and evolutionary algorithms.


pdf

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doi

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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 (0.698), 2013 (0.235), 2012 (0.370), 2011 (0.340), 2010 (0.475), 2009 (0.325), 2008 (0.439), 2007 (0.684)

URL

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Bibtex

@ARTICLE{tskn2007, author = {G. Terrazas and P. Siepmann and G. Kendall and N. Krasnogor},
title = {An Evolutionary Methodology for the Automated Design of Cellular Automaton-based Complex Systems},
journal = {Journal of Cellular Automata},
year = {2007},
volume = {2},
pages = {77--102},
number = {1},
abstract = {Cellular automata (CA) are an important modelling paradigm in the natural sciences and an extremely useful approach in the study of complex systems. Homogeneity, massive parallelism, local cellular interactions and both synchronous and asynchronous models of rule execution are some of their most prominent features, allowing scientists to model and understand a variety of phenomena in, to name but a few, the physical, chemical, biological, social and information sciences. An ubiquitous problem related with the study of complex systems by means of CA is that of parameter identification. In some cases, analytical methods are available but in many others, due to the bottom-up complexity of the underlying processes, the best route for CA identification is through design optimization by means of a metaheuristic, such as an evolutionary algorithm. In this work we report on a systematic methodology we have developed to control the spatio-temporal behavior of a CA in order to obtain a ‘designoid’ target pattern. Four independent CA-based complex systems were used to assess our hypothesis which combines clustering, fitness distance correlation and evolutionary algorithms.},
issn = {1557-5969},
keywords = {Cellular Automata, Complex Systems, evolutionary algorithms, meta-heuristics, metaheuristics},
timestamp = {2007.05.22},
webpdf = {http://www.graham-kendall.com/tskn2007.pdf} }