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 football fixtures worked out?
http://bit.ly/1z0oTAH
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

Tracking Paper Downloads: Database

Publication(s)

On Nash equilibrium and evolutionarily stable states that are not characterised by the folk theorem
http://bit.ly/1J4KNC0
Evolutionary Stability of Discriminating Behaviors With the Presence of Kin Cheaters
http://bit.ly/1lcxoOK
An Exponential Monte-Carlo Local Search Algorithm for the Berth Allocation Problem
http://bit.ly/1tLKIMh
Chapter 4: Genetic Algorithms
http://bit.ly/1sYEs1Q

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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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} }