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?
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What do we spend so much in supermarkets?
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Latest Blog Post

Snooker: Celebrating 40 years at the Crucible

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Update: Displaying bibtex on web site

Publication(s)

Universiti Malaysia Pahang examination timetabling problem: scheduling invigilators
RATE_LIMIT_EXCEEDED
Repeated Goofspiel: A Game of Pure Strategy
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A great deluge algorithm for a real-world examination timetabling problem
RATE_LIMIT_EXCEEDED
A Study of Simulated Annealing Hyperheuristics
RATE_LIMIT_EXCEEDED

Graham Kendall: Details of Requested Publication


Citation

Burke, E. K.; Gustafson, S.; Kendall, G. and Krasnogor, N. Advanced Population Diversity Measures in Genetic Progaramming. In Proceedings of Parallel Problem Solving from Nature (PPSN VII), pages 341-350, Springer, Granada, Spain, 7-11 September, Lecture Notes in Computer Science 2439, 2002.

This paper incorrectly cited P. D'haeseleer 1994 paper on context preserving crossover. The correct citation should be the "Effects of Locality in Individual Population Evolution" in Advances in Genetic Programming, 1994, edited by K.E Kinnear Jr.


Abstract

This paper presents a survey and comparison of significant diversity measures in the genetic programming literature. This study builds on previous work by the authors to gain a deeper understanding of the conditions under which genetic programming evolution is successful. Three benchmark problems (Artificial Ant, Symbolic Regression and Even-5-Parity) are used to illustrate different diversity measures and to analyse their correlation with performance. Results show that measures of population diversity based on edit distances and phenotypic diversity suggest that successful evolution occurs when populations converge to a similar structure but with high fitness diversity.


pdf

You can download the pdf of this publication from here


doi

The doi for this publication is 10.1007/3-540-45712-7_33 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



URL

The URL for additional information is http://dx.doi.org/10.1007/3-540-45712-7

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

@INPROCEEDINGS{bgkk2002, author = {Burke, Edmund K. and Gustafson, Steven and Kendall, Graham and Krasnogor, Natalio},
title = {Advanced Population Diversity Measures in Genetic Progaramming},
booktitle = {Proceedings of Parallel Problem Solving from Nature (PPSN VII)},
year = {2002},
editor = {J. J. Merelo and P. Adamidis and H-G. Beyer},
volume = {2439},
series = {Lecture Notes in Computer Science},
pages = {341--350},
address = {Granada, Spain, 7-11 September},
publisher = {Springer},
note = {This paper incorrectly cited P. D'haeseleer 1994 paper on context preserving crossover. The correct citation should be the "Effects of Locality in Individual Population Evolution" in Advances in Genetic Programming, 1994, edited by K.E Kinnear Jr.},
abstract = {This paper presents a survey and comparison of significant diversity measures in the genetic programming literature. This study builds on previous work by the authors to gain a deeper understanding of the conditions under which genetic programming evolution is successful. Three benchmark problems (Artificial Ant, Symbolic Regression and Even-5-Parity) are used to illustrate different diversity measures and to analyse their correlation with performance. Results show that measures of population diversity based on edit distances and phenotypic diversity suggest that successful evolution occurs when populations converge to a similar structure but with high fitness diversity.},
doi = {10.1007/3-540-45712-7_33},
keywords = {genetic programming, diversity measures, Artificial Ant, Symbolic Regression, Even-5-Parity},
timestamp = {2007.03.29},
url = {http://dx.doi.org/10.1007/3-540-45712-7},
webpdf = {http://www.graham-kendall.com/papers/bgkk2002.pdf} }