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

Help solve Santa's logistics problems
http://bit.ly/1DXreuW
I have published a few papers on Sports Scheduling.
http://bit.ly/gVaUqT

Latest Blog Post

How Isaac Newton could help you beat the casino at roulette

Random Blog Post

2010 Pac-Man Competition at CIG 2010

Publication(s)

The Effects of Extra-Somatic Weapons on the Evolution of Human Cooperation towards Non-Kin
http://bit.ly/1oXDe7O
An Investigation, using Co-Evolution, to Evolve an Awari Player
http://bit.ly/dVtkw8
Applying Ant Algorithms and the No Fit Polygon to the Nesting Problem
http://bit.ly/gkQlW3
Artificial and Computational Intelligence for Games on Mobile Platforms
http://bit.ly/1hWKtsE

Graham Kendall: Details of Requested Publication


Citation

Gustafson, S; Ekart, A; Burke, E and Kendall, G Problem Difficulty and Code Growth in Genetic Programming. Genetic Programming and Evolvable Machines, 5 (3): 271-290, 2004.


Abstract

This paper investigates the relationship between code growth and problem difficulty in genetic programming. The symbolic regression problem domain is used to investigate this relationship using two different types of increased instance difficulty. Results are supported by a simplified model of genetic programming and show that increased difficulty induces higher selection pressure and less genetic diversity, which both contribute toward an increased rate of code growth.


pdf

You can download the pdf of this publication from here


doi

The doi for this publication is 10.1023/B:GENP.0000030194.98244.e3 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 (0.903), 2013 (1.065), 2012 (1.333), 2011 (1.000), 2010 (1.167), 2009 (1.091)

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{gebk2004, author = {S. Gustafson and A. Ekart and E. Burke and G. Kendall},
title = {Problem Difficulty and Code Growth in Genetic Programming},
journal = {Genetic Programming and Evolvable Machines},
year = {2004},
volume = {5},
pages = {271--290},
number = {3},
month = {September 2004},
abstract = {This paper investigates the relationship between code growth and problem difficulty in genetic programming. The symbolic regression problem domain is used to investigate this relationship using two different types of increased instance difficulty. Results are supported by a simplified model of genetic programming and show that increased difficulty induces higher selection pressure and less genetic diversity, which both contribute toward an increased rate of code growth.},
doi = {10.1023/B:GENP.0000030194.98244.e3},
issn = {1389-2576},
keywords = {genetic programming, population diversity, code growth, problem difficulty},
webpdf = {http://www.graham-kendall.com/papers/gebk2004.pdf} }