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 are university examinations scheduled?
http://bit.ly/1z0pG4s
I am a member of the Automated Scheduling, Optimisation and Planning Research Group
http://bit.ly/eIQ5XC

Latest Blog Post

How Isaac Newton could help you beat the casino at roulette

Random Blog Post

We should be just a number, and we should embrace it: Extend the use of ORCID?

Publication(s)

Maintaining regularity and generalization in data using the minimum description length principle and genetic algorithm: Case of grammatical inference
http://bit.ly/2hOrTMS
Chapter 7: Opponent Modelling, Evolution, and the Iterated Prisoner's Dilemma
http://bit.ly/1eJzj8n
Computing Nash Equilibria and Evolutionarily Stable States of Evolutionary Games
http://bit.ly/24qGpHI
An efficient and robust approach to generate high quality solutions for the Traveling Tournament Problem.
http://bit.ly/fCqNU6

Graham Kendall: Details of Requested Publication


Citation

Maashi, M; Kendall, G and Özcan, E Choice Function based Hyper-heuristics for Multi-objective Optimization. Applied Soft Computing, 28: 312-326, 2015.

ISSN: 1568-4946


Abstract

A selection hyper-heuristic is a high level search methodology which operates over a fixed set of low level heuristics. During the iterative search process, a heuristic is selected and applied to a candidate solution in hand, producing a new solution which is then accepted or rejected at each step. Selection hyper-heuristics have been increasingly, and successfully, applied to single-objective optimization problems, while work on multi-objective selection hyper-heuristics is limited. This work presents one of the initial studies on selection hyper-heuristics combining a choice function heuristic selection methodology with great deluge and late acceptance as nondeterministic move acceptance methods for multi-objective optimization. A well known hypervolume metric is integrated into the move acceptance methods to enable the approaches to deal with multi-objective problems. The performance of the proposed hyper-heuristics is investigated on the Walking Fish Group test suite which is a common benchmark for multi-objective optimization. Additionally, they are applied to the vehicle crashworthiness design problem as a real-world multi-objective problem. The experimental results demonstrate the effectiveness of the non-deterministic move acceptance, particularly great deluge when used as a component of a choice function based selection hyper-heuristic.


pdf

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doi

The doi for this publication is 10.1016/j.asoc.2014.12.012 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

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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{mko2015, author = {M. Maashi and G. Kendall and E. \"{O}zcan},
title = {Choice Function based Hyper-heuristics for Multi-objective Optimization},
journal = {Applied Soft Computing},
year = {2015},
volume = {28},
pages = {312--326},
note = {ISSN: 1568-4946},
abstract = {A selection hyper-heuristic is a high level search methodology which operates over a fixed set of low level heuristics. During the iterative search process, a heuristic is selected and applied to a candidate solution in hand, producing a new solution which is then accepted or rejected at each step. Selection hyper-heuristics have been increasingly, and successfully, applied to single-objective optimization problems, while work on multi-objective selection hyper-heuristics is limited. This work presents one of the initial studies on selection hyper-heuristics combining a choice function heuristic selection methodology with great deluge and late acceptance as nondeterministic move acceptance methods for multi-objective optimization. A well known hypervolume metric is integrated into the move acceptance methods to enable the approaches to deal with multi-objective problems. The performance of the proposed hyper-heuristics is investigated on the Walking Fish Group test suite which is a common benchmark for multi-objective optimization. Additionally, they are applied to the vehicle crashworthiness design problem as a real-world multi-objective problem. The experimental results demonstrate the effectiveness of the non-deterministic move acceptance, particularly great deluge when used as a component of a choice function based selection hyper-heuristic.},
doi = {10.1016/j.asoc.2014.12.012},
issn = {1568-4946},
owner = {Graham},
timestamp = {2014.01.28},
webpdf = {http://www.graham-kendall.com/papers/mok2015.pdf} }