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 blog occasionally, feel free to take a look.
http://bit.ly/hq6rMK
The hunt for MH370
http://bit.ly/1DXRLbu

Latest Blog Post

How Isaac Newton could help you beat the casino at roulette

Random Blog Post

Predicting the Results of Football Matches

Publication(s)

Grammatical Evolution of Local Search Heuristics
http://bit.ly/1c3ciu6
A Multi-objective Hyper-heuristic based on Choice Function
http://bit.ly/1f8GQgU
Chapter 1: Introduction
http://bit.ly/1goQv51
Document Zone Classification for Technical Document Images Using Artificial Neural Network and Support Vector Machines
http://bit.ly/1eUn8rs

Graham Kendall: Details of Requested Publication


Citation

Hallam, N; Blanchfield, P and Kendall, G Handling diversity in evolutionary multiobjective optimization. In Proceedings of the 2005 IEEE Congress on Evolutionary Computation, pages 2233-2240, 2005.


Abstract

In evolutionary multiobjective optimisation (EMO), the diversity of the set of non-dominated solutions used to be handled by the niching and fitness sharing technique. The main downside of this technique is the need to set the niche radius. Quite recently, new techniques have emerged and proved to be more successful. The grid-based density of the adaptive grid algorithm (AGA), the crowding-distance technique of the nondominated sorting genetic algorithm (NSGA-II), and the archive truncation procedure of the strength Pareto evolutionary algorithm (SPEA2) are the latest successful methods that ensure a better diversity than the traditional less effective and computationally expensive niching method. In this work, a crowding-dispersion technique which is based on the Pareto potential regions (PPR), is proposed and compared to three recent techniques.


pdf

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doi

The doi for this publication is 10.1109/CEC.2005.1554972 You can link directly to the original paper, via the doi, from here

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URL

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Bibtex

@INPROCEEDINGS{hpk2005, author = {N. Hallam and P. Blanchfield and G. Kendall},
title = {Handling diversity in evolutionary multiobjective optimization},
booktitle = {Proceedings of the 2005 IEEE Congress on Evolutionary Computation},
year = {2005},
pages = {2233--2240},
abstract = {In evolutionary multiobjective optimisation (EMO), the diversity of the set of non-dominated solutions used to be handled by the niching and fitness sharing technique. The main downside of this technique is the need to set the niche radius. Quite recently, new techniques have emerged and proved to be more successful. The grid-based density of the adaptive grid algorithm (AGA), the crowding-distance technique of the nondominated sorting genetic algorithm (NSGA-II), and the archive truncation procedure of the strength Pareto evolutionary algorithm (SPEA2) are the latest successful methods that ensure a better diversity than the traditional less effective and computationally expensive niching method. In this work, a crowding-dispersion technique which is based on the Pareto potential regions (PPR), is proposed and compared to three recent techniques.},
doi = {10.1109/CEC.2005.1554972},
keywords = {evolutionary algorithm, multi-objective},
owner = {gxk},
timestamp = {2011.01.02},
webpdf = {http://www.graham-kendall.com/papers/hpk2005.pdf} }