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 university examinations scheduled?
http://bit.ly/1z0pG4s
I have published a few papers on Sports Scheduling.
http://bit.ly/gVaUqT

Latest Blog Post

Snooker: Celebrating 40 years at the Crucible

Random Blog Post

Research Assessment in the UK

Publication(s)

Heuristic, meta-heuristic and hyper-heuristic approaches for fresh produce inventory control and shelf space allocation
http://bit.ly/dH42Fp
Fuzzy job shop scheduling with lot-sizing
http://bit.ly/gnd5ds
The importance of a piece difference feature to Blondie24
http://bit.ly/1a2Ns0W
Hyper-heuristics: a survey of the state of the art
http://bit.ly/1eSDAeb

Graham Kendall: Details of Requested Publication


Citation

Burke, E.K; Gustafson, S and Kendall, G Diversity in Genetic Programming: An Analysis of Measures and Correlation with Fitness. IEEE Transactions on Evolutionary Computation, 8 (1): 47-62, 2004.


Abstract

Examines measures of diversity in genetic programming. The goal is to understand the importance of such measures and their relationship with fitness. Diversity methods and measures from the literature are surveyed and a selected set of measures are applied to common standard problem instances in an experimental study. Results show the varying definitions and behaviors of diversity and the varying correlation between diversity and fitness during different stages of the evolutionary process. Populations in the genetic programming algorithm are shown to become structurally similar while maintaining a high amount of behavioral differences. Conclusions describe what measures are likely to be important for understanding and improving the search process and why diversity might have different meaning for different problem domains.


pdf

You can download the pdf of this publication from here


doi

The doi for this publication is 10.1109/TEVC.2003.819263 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 (3.654), 2013 (5.545), 2012 (4.810), 2011 (3.341), 2010 (4.403), 2009 (4.589), 2008 (3.736), 2007 (2.426), 2006 (3.770), 2005 (3.257), 2004 (3.688), 2003 (2.713), 2002 (1.486), 2001 (1.708)

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{bgk2004, author = {E.K. Burke and S. Gustafson and G. Kendall},
title = {Diversity in Genetic Programming: An Analysis of Measures and Correlation with Fitness},
journal = {IEEE Transactions on Evolutionary Computation},
year = {2004},
volume = {8},
pages = {47--62},
number = {1},
month = {February 2004},
abstract = {Examines measures of diversity in genetic programming. The goal is to understand the importance of such measures and their relationship with fitness. Diversity methods and measures from the literature are surveyed and a selected set of measures are applied to common standard problem instances in an experimental study. Results show the varying definitions and behaviors of diversity and the varying correlation between diversity and fitness during different stages of the evolutionary process. Populations in the genetic programming algorithm are shown to become structurally similar while maintaining a high amount of behavioral differences. Conclusions describe what measures are likely to be important for understanding and improving the search process and why diversity might have different meaning for different problem domains.},
date-modified = {2007-01-18 13:04:57 +0000},
doi = {10.1109/TEVC.2003.819263},
issn = {1089-778X},
keywords = {Genetic Programming, Diversity},
webpdf = {http://www.graham-kendall.com/papers/bgk2004.pdf} }