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 have wriiten a number of articles for TheConversation
http://bit.ly/1yWlOkE
I am involved with a spin out company that specialises in Strategic Resource Planning
http://bit.ly/eTPZO2

Latest Blog Post

Snooker: Celebrating 40 years at the Crucible

Random Blog Post

Christmas 2015: Advent calendar of research

Publication(s)

Evaluating the performance of a EuroDivisia index using artificial intelligence techniques
http://bit.ly/gaswDm
A task based approach for a real-world commodity routing problem
http://bit.ly/1mlrzCS
We should be just a number, and we should embrace it
http://bit.ly/2mRCd5m
Chapter 4: Genetic Algorithms
http://bit.ly/1sYEs1Q

Graham Kendall: Details of Requested Publication


Citation

Sabar, N. R and Kendall, G Using Harmony Search with Multiple Pitch Adjustment Operators for the Portfolio Selection Problem. In Proceedings of the 2014 IEEE Congress on Evolutionary Computation (CEC 2014), pages 499-503, 2014.


Abstract

Portfolio selection is an important problem in the financial markets that seeks to distribute an amount of money over a set of assets where the goal is to simultaneously maximize the return and minimize the risk. In this work, we propose a harmony search algorithm (HSA) for this problem. HSA is a population based algorithm that mimics the musician improvisation process in solving optimization problems. At each iteration, HSA generates a new solution using a memory procedure which considers all existing solutions and then perturbs them using a pitch adjustment operator. To deal with different instances, and also changes in the problem landscape, we propose an improved HSA that utilizes multiple pitch adjustment operators. The rationale behind this is that different operators are appropriate for different stages of the search and using multiple operators can enhance the effectiveness of HSA. To evaluate and validate the effectiveness of the proposed HSA, computational experiments are carried out using portfolio selection benchmark instances from the scientific literature. The results demonstrate that the proposed HSA is capable of producing high quality solutions for most of the tested instances when compared with state of the art methods.


pdf

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doi

The doi for this publication is 10.1109/CEC.2014.6900384 You can link directly to the original paper, via the doi, from here

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Bibtex

@INPROCEEDINGS{sk2014b, author = {N. R. Sabar and G. Kendall},
title = {Using Harmony Search with Multiple Pitch Adjustment Operators for the Portfolio Selection Problem},
booktitle = {Proceedings of the 2014 IEEE Congress on Evolutionary Computation (CEC 2014)},
year = {2014},
pages = {499--503},
abstract = {Portfolio selection is an important problem in the financial markets that seeks to distribute an amount of money over a set of assets where the goal is to simultaneously maximize the return and minimize the risk. In this work, we propose a harmony search algorithm (HSA) for this problem. HSA is a population based algorithm that mimics the musician improvisation process in solving optimization problems. At each iteration, HSA generates a new solution using a memory procedure which considers all existing solutions and then perturbs them using a pitch adjustment operator. To deal with different instances, and also changes in the problem landscape, we propose an improved HSA that utilizes multiple pitch adjustment operators. The rationale behind this is that different operators are appropriate for different stages of the search and using multiple operators can enhance the effectiveness of HSA. To evaluate and validate the effectiveness of the proposed HSA, computational experiments are carried out using portfolio selection benchmark instances from the scientific literature. The results demonstrate that the proposed HSA is capable of producing high quality solutions for most of the tested instances when compared with state of the art methods.},
doi = {10.1109/CEC.2014.6900384},
owner = {Graham},
timestamp = {2014.08.08},
webpdf = {http://www.graham-kendall.com/papers/sk2014b.pdf} }