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
How are university examinations scheduled?
http://bit.ly/1z0pG4s

Latest Blog Post

How Isaac Newton could help you beat the casino at roulette

Random Blog Post

Singapore Statement on Research Integrity

Publication(s)

Complete and robust no-fit polygon generation for the irregular stock cutting problem
http://bit.ly/fwKSfE
Evolving Neural Networks with Evolutionary Strategies: A New Application to Divisa Money
http://bit.ly/dKzEAy
The importance of a piece difference feature to Blondie24
http://bit.ly/1a2Ns0W
An Investigation of an Adaptive Poker player
http://bit.ly/grGqmI

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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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)

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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} }