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 the chair of the MISTA (Multidisciplinary International Conference on Scheduling: Theory and Applications)
http://bit.ly/hvZIaN
How are university examinations scheduled?
http://bit.ly/1z0pG4s

Latest Blog Post

How Isaac Newton could help you beat the casino at roulette

Random Blog Post

Parsing Bibtex Authors: How I Do It

Publication(s)

Introducing a Round Robin Tournament into Evolutionary Individual and Social Learning Checkers
http://bit.ly/1cJw2am
Regulators as agents: Modelling personality and power as evidence is brokered to support decisions on environmental risk
http://bit.ly/1bh6em7
Scheduling English Football Fixtures over the Holiday Period Using Hyper-heuristics
http://bit.ly/eeIVyB
Real-time Scheduling for Multi Headed Placement Machine
http://bit.ly/eVWnGn

Graham Kendall: Details of Requested Publication


Citation

Bai, R; Blazewicz, J; Burke, E. K; Kendall, G and McCollum, B A Simulated Annealing Hyper-heuristic Methodology for Flexible Decision Support. 4OR - A Quarterly Journal of Operations Research, 10 (1): 43-66, 2012.


Abstract

Most of the current search techniques represent approaches that are largely adapted for specific search problems. There are many real-world scenarios where the development of such bespoke systems is entirely appropriate. However, there are other situations where it would be beneficial to have methodologies which are generally applicable to more problems. One of our motivating goals for investigating hyper-heuristic methodologies is to provide a more general search framework that can be easily and automatically employed on a broader range of problems than is currently possible. In this paper, we investigate a simulated annealing hyper-heuristic methodology which operates on a search space of heuristics and which employs a stochastic heuristic selection strategy and a short-term memory. The generality and performance of the proposed algorithm is demonstrated over a large number of benchmark datasets drawn from two very different and difficult problems, namely; course timetabling and bin packing. The contribution of this paper is to present a method which can be readily (and automatically) applied to different problems whilst still being able to produce results on benchmark problems which are competitive with bespoke human designed tailor made algorithms for those problems.


pdf

You can download the pdf of this publication from here


doi

The doi for this publication is 10.1007/s10288-011-0182-8 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 (1.000), 2013 (0.918), 2012 (0.730), 2011 (0.323), 2010 (0.690), 2009 (0.750)

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{bbbkm2099, author = {R. Bai and J. Blazewicz and E. K. Burke and G. Kendall and B. McCollum},
title = {A Simulated Annealing Hyper-heuristic Methodology for Flexible Decision Support},
journal = {4OR - A Quarterly Journal of Operations Research},
year = {2012},
volume = {10},
pages = {43--66},
number = {1},
abstract = {Most of the current search techniques represent approaches that are largely adapted for specific search problems. There are many real-world scenarios where the development of such bespoke systems is entirely appropriate. However, there are other situations where it would be beneficial to have methodologies which are generally applicable to more problems. One of our motivating goals for investigating hyper-heuristic methodologies is to provide a more general search framework that can be easily and automatically employed on a broader range of problems than is currently possible. In this paper, we investigate a simulated annealing hyper-heuristic methodology which operates on a search space of heuristics and which employs a stochastic heuristic selection strategy and a short-term memory. The generality and performance of the proposed algorithm is demonstrated over a large number of benchmark datasets drawn from two very different and difficult problems, namely; course timetabling and bin packing. The contribution of this paper is to present a method which can be readily (and automatically) applied to different problems whilst still being able to produce results on benchmark problems which are competitive with bespoke human designed tailor made algorithms for those problems.},
doi = {10.1007/s10288-011-0182-8},
issn = {1619-4500},
keywords = {hyper-heuristic, hyperheuristic, simulated annealing, bin packing, packing, timetabling, course timetabling},
owner = {gxk},
timestamp = {2011.07.07},
webpdf = {http://www.graham-kendall.com/papers/bbbkm2012.pdf} }