Graham Kendall
Various Images

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 a member of the Automated Scheduling, Optimisation and Planning Research Group
http://bit.ly/eIQ5XC
If you are interested in hyper-heuristics, take a look at my publications in this area
http://bit.ly/efxLGg

Latest Blog Post

Snooker: Celebrating 40 years at the Crucible

Random Blog Post

Wisdom of the Crowds at the Graduate School Christmas Party

Publication(s)

The Application of a Dendritic Cell Algorithm to a Robotic Classifier
http://bit.ly/hTMQ5K
Document Zone Classification for Technical Document Images Using Artificial Neural Network and Support Vector Machines
http://bit.ly/1eUn8rs
Hybrid Heuristic for Multi-carrier Transportation Plans
http://bit.ly/1dGGwqO
Regulators as agents: Modelling personality and power as evidence is brokered to support decisions on environmental risk
http://bit.ly/1bh6em7

Graham Kendall: Details of Requested Publication


Citation

Hallam, N; Kendall, G and Blanchfield, P Solving Multi-objective Optimisation Problems Using the Potential Pareto Regions Evolutionary Algorithm. In Proceedings of the 9th International Conference on Parallel Problem Solving from Nature (PPSN 2006), pages 503-512, Lecture Notes in Computer Science 4193, 2006.


Abstract

In this paper we propose a novel multi-objective evolutionary algorithm that we call Potential Pareto Regions Evolutionary Algorithm (PPREA). Unlike state-of-the-art algorithms, which use a fitness assignment method based on Pareto ranking, the approach adopted in this work is new. The fitness of an individual is equal to the least improvement needed by that individual in order to reach non-dominance status. This new algorithm is compared against the Nondominated Sorting Genetic Algorithm (NSGA-II) on a set of test suite problems derived from the works of researchers from MOEA community.


pdf

You can download the pdf of this publication from here


doi

The doi for this publication is 10.1007/11844297_51 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

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

@INPROCEEDINGS{hkb2006, author = {N. Hallam and G. Kendall and P. Blanchfield},
title = {Solving Multi-objective Optimisation Problems Using the Potential Pareto Regions Evolutionary Algorithm},
booktitle = {Proceedings of the 9th International Conference on Parallel Problem Solving from Nature (PPSN 2006)},
year = {2006},
editor = {T.P. Runarsson and H-G. Beyer and E.K. Burke and G. Merelo Guervos and J. Juan and D. Whitley and X. Yao},
volume = {4193},
series = {Lecture Notes in Computer Science},
pages = {503--512},
month = {September},
organization = {Reykjavik, Iceland},
abstract = {In this paper we propose a novel multi-objective evolutionary algorithm that we call Potential Pareto Regions Evolutionary Algorithm (PPREA). Unlike state-of-the-art algorithms, which use a fitness assignment method based on Pareto ranking, the approach adopted in this work is new. The fitness of an individual is equal to the least improvement needed by that individual in order to reach non-dominance status. This new algorithm is compared against the Nondominated Sorting Genetic Algorithm (NSGA-II) on a set of test suite problems derived from the works of researchers from MOEA community.},
doi = {10.1007/11844297_51},
keywords = {multi-objective. NSGA II, pareto},
owner = {gxk},
timestamp = {2007.03.29},
webpdf = {http://www.graham-kendall.com/papers/hkb2006.pdf} }