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 to teach Deep Blue to play poker and deliver groceries
http://bit.ly/1DXGeZD
A Conversation article celebrating Pi
http://bit.ly/1DXuXbV

Latest Blog Post

How Isaac Newton could help you beat the casino at roulette

Random Blog Post

AlphaGo: Computers and Game Playing: A Very Timely Lecture

Publication(s)

An investigation of a hyperheuristic genetic algorithm applied to a trainer scheduling problem
http://bit.ly/egxt0d
Evolutionary Stability of Discriminating Behaviors With the Presence of Kin Cheaters
http://bit.ly/1lcxoOK
Automatic Design of Hyper-heuristic Framework with Gene Expression Programming for Combinatorial Optimization problems
http://bit.ly/1L6OJ8g
Evaluating decision-making units under uncertainty using fuzzy multi-objective nonlinear programming
http://bit.ly/2k79ATE

Graham Kendall: Details of Requested Publication


Citation

Lwin, K; Qu, R and Kendall, G A learning-guided multi-objective evolutionary algorithm for constrained portfolio optimization. Applied Soft Computing, 24: 757-772, 2014.

ISSN: 1568-4946


Abstract

Portfolio optimization involves the optimal assignment of limited capital to different available financial assets to achieve a reasonable trade-off between profit and risk objectives. In this paper, we studied the extended Markowitz's mean-variance portfolio optimization model. We considered the cardinality, quantity, pre-assignment and round lot constraints in the extended model. These four real-world constraints limit the number of assets in a portfolio, restrict the minimum and maximum proportions of assets held in the portfolio, require some specific assets to be included in the portfolio and require to invest the assets in units of a certain size respectively. An efficient learning-guided hybrid multi-objective evolutionary algorithm is proposed to solve the constrained portfolio optimization problem in the extended mean-variance framework. A learning-guided solution generation strategy is incorporated into the multi-objective optimization process to promote the efficient convergence by guiding the evolutionary search towards the promising regions of the search space. The proposed algorithm is compared against four existing state-of-the-art multi-objective evolutionary algorithms, namely Non-dominated Sorting Genetic Algorithm (NSGA-II), Strength Pareto Evolutionary Algorithm (SPEA-2), Pareto Envelope-based Selection Algorithm (PESA-II) and Pareto Archived Evolution Strategy (PAES). Computational results are reported for publicly available OR-library datasets from seven market indices involving up to 1318 assets. Experimental results on the constrained portfolio optimization problem demonstrate that the proposed algorithm significantly outperforms the four well-known multi-objective evolutionary algorithms with respect to the quality of obtained efficient frontier in the conducted experiments.


pdf

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doi

The doi for this publication is 10.1016/j.asoc.2014.08.026 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


Journal Rankings


ISI Web of Knowledge Journal Citation Reports

The Web of Knowledge Journal Citation Reports (often known as ISI Impact Factors) help measure how often an article is cited. You can get an introduction to Journal Citation Reports here. Below I have provided the ISI impact factor for the jourrnal in which this article was published. For complete information I have shown the ISI ranking over a number of years, with the latest ranking highlighted.

2014 (2.810), 2013 (2.679), 2012 (2.526), 2011 (2.612), 2010 (2.097), 2009 (2.415), 2008 (1.909), 2007 (1.537), 2006 (0.849)

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

@ARTICLE{lrk2014, author = {K. Lwin and R. Qu and G. Kendall},
title = {A learning-guided multi-objective evolutionary algorithm for constrained portfolio optimization},
journal = {Applied Soft Computing},
year = {2014},
volume = {24},
pages = {757--772},
note = {ISSN: 1568-4946},
abstract = {Portfolio optimization involves the optimal assignment of limited capital to different available financial assets to achieve a reasonable trade-off between profit and risk objectives. In this paper, we studied the extended Markowitz's mean-variance portfolio optimization model. We considered the cardinality, quantity, pre-assignment and round lot constraints in the extended model. These four real-world constraints limit the number of assets in a portfolio, restrict the minimum and maximum proportions of assets held in the portfolio, require some specific assets to be included in the portfolio and require to invest the assets in units of a certain size respectively. An efficient learning-guided hybrid multi-objective evolutionary algorithm is proposed to solve the constrained portfolio optimization problem in the extended mean-variance framework. A learning-guided solution generation strategy is incorporated into the multi-objective optimization process to promote the efficient convergence by guiding the evolutionary search towards the promising regions of the search space. The proposed algorithm is compared against four existing state-of-the-art multi-objective evolutionary algorithms, namely Non-dominated Sorting Genetic Algorithm (NSGA-II), Strength Pareto Evolutionary Algorithm (SPEA-2), Pareto Envelope-based Selection Algorithm (PESA-II) and Pareto Archived Evolution Strategy (PAES). Computational results are reported for publicly available OR-library datasets from seven market indices involving up to 1318 assets. Experimental results on the constrained portfolio optimization problem demonstrate that the proposed algorithm significantly outperforms the four well-known multi-objective evolutionary algorithms with respect to the quality of obtained efficient frontier in the conducted experiments.},
doi = {10.1016/j.asoc.2014.08.026},
issn = {1568-4946},
owner = {Graham},
timestamp = {2014.01.28},
webpdf = {http://www.graham-kendall.com/papers/lrk2014.pdf} }