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 am involved with a spin out company that specialises in Strategic Resource Planning
http://bit.ly/eTPZO2
I have published some papers on timetabling.
http://bit.ly/hSGAhZ

Latest Blog Post

Snooker: Celebrating 40 years at the Crucible

Random Blog Post

Parsing Bibtex Authors: How I Do It

Publication(s)

Sampling of Unique Structures and Behaviours in Genetic Programming
http://bit.ly/ehhZrr
Scheduling in sports: An annotated bibliography
http://bit.ly/eCfi42
An investigation of a tabu assisted hyper-heuristic genetic algorithm
http://bit.ly/e1WFfU
The Limitations of Frequency Analysis for Dendritic Cell Population Modelling
http://bit.ly/h5VYIQ

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} }