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 a few papers on Sports Scheduling.
http://bit.ly/gVaUqT

Latest Blog Post

Snooker: Celebrating 40 years at the Crucible

Random Blog Post

Tracking Paper Downloads: Database

Publication(s)

A Study of Simulated Annealing Hyperheuristics
http://bit.ly/ifevCO
Automated code generation by local search
http://bit.ly/1dAz69a
Using Harmony Search with Multiple Pitch Adjustment Operators for the Portfolio Selection Problem
http://bit.ly/1okj5eC
A Strategy with Novel Evolutionary Features for the Iterated Prisoner's Dilemma
http://bit.ly/eURggX

Graham Kendall: Details of Requested Publication


Citation

Su, Y and Kendall, G A Particle Swarm Optimisation Approach in the Construction of Optimal Risky Portfolios. In Proceedings of the 23rd IASTED International Multi-Conference Artificial Intelligence and Applications, pages 140-145, 2005.


Abstract

In this paper, we apply particle swarm optimisation to the construction of optimal risky portfolios for financial investments. Constructing an optimal risky portfolio is a high-dimensional constrained optimisation problem where financial investors look for an optimal combination of their investments among different financial assets with the aim of achieving a maximum reward-to-variability ratio. A particle swarm solver is developed and tested on various restricted and unrestricted risky investment portfolios. The particle swarm solver demonstrates high computational efficiency in constructing optimal risky portfolios of less than fifteen assets. The effectiveness of a weighting function in the particle swarm optimisation algorithm is also studied


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Bibtex

@INPROCEEDINGS{sk2005, author = {Y. Su and G. Kendall},
title = {A Particle Swarm Optimisation Approach in the Construction of Optimal Risky Portfolios},
booktitle = {Proceedings of the 23rd IASTED International Multi-Conference Artificial Intelligence and Applications},
year = {2005},
pages = {140--145},
abstract = {In this paper, we apply particle swarm optimisation to the construction of optimal risky portfolios for financial investments. Constructing an optimal risky portfolio is a high-dimensional constrained optimisation problem where financial investors look for an optimal combination of their investments among different financial assets with the aim of achieving a maximum reward-to-variability ratio. A particle swarm solver is developed and tested on various restricted and unrestricted risky investment portfolios. The particle swarm solver demonstrates high computational efficiency in constructing optimal risky portfolios of less than fifteen assets. The effectiveness of a weighting function in the particle swarm optimisation algorithm is also studied},
keywords = {particle swarm optimisation, portfolio, financial investments},
owner = {gxk},
timestamp = {2011.01.03},
webpdf = {http://www.graham-kendall.com/papers/sk2005.pdf} }