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 published some papers on timetabling.
http://bit.ly/hSGAhZ
How are university examinations scheduled?
http://bit.ly/1z0pG4s

Latest Blog Post

Snooker: Celebrating 40 years at the Crucible

Random Blog Post

Google Scholar

Publication(s)

The Effects of Extra-Somatic Weapons on the Evolution of Human Cooperation towards Non-Kin
http://bit.ly/1oXDe7O
A multi-agent based simulated stock market - testing on different types of stocks
http://bit.ly/haEt18
Maximising Stadium Attendance: A Case Study of Malaysian Football
http://bit.ly/eFTURu
Evolutionary Computation in the Real World: Successes and Challenges
http://bit.ly/1tT0uEY

Graham Kendall: Details of Requested Publication


Citation

Burke, E. K; Kendall, G; Misir, M and Özcan, E Monte Carlo hyper-heuristics for examination timetabling. Annals of Operations Research, 196 (1): 73-90, 2012.


Abstract

Automating the neighbourhood selection process in an iterative approach that uses multiple heuristics is not a trivial task. Hyper-heuristics are search methodologies that not only aim to provide a general framework for solving problem instances at different difficulty levels in a given domain, but a key goal is also to extend the level of generality so that different problems from different domains can also be solved. Indeed, a major challenge is to explore how the heuristic design process might be automated. Almost all existing iterative selection hyper-heuristics performing single point search contain two successive stages; heuristic selection and move acceptance. Different operators can be used in either of the stages. Recent studies explore ways of introducing learning mechanisms into the search process for improving the performance of hyper-heuristics. In this study, a broad empirical analysis is performed comparing Monte Carlo based hyper-heuristics for solving capacitated examination timetabling problems. One of these hyper-heuristics is an approach that overlaps two stages and presents them in a single algorithmic body. A learning heuristic selection method (L) operates in harmony with a simulated annealing move acceptance method using reheating (SA) based on some shared variables. Yet, the heuristic selection and move acceptance methods can be separated as the proposed approach respects the common selection hyper-heuristic framework. The experimental results show that simulated annealing with reheating as a hyper-heuristic move acceptance method has significant potential. On the other hand, the learning hyper-heuristic using simulated annealing with reheating move acceptance (LSA) performs poorly due to certain weaknesses, such as the choice of rewarding mechanism and the evaluation of utility values for heuristic selection as compared to some other hyper-heuristics in examination timetabling. Trials with other heuristic selection methods confirm that the best alternative for the simulated annealing with reheating move acceptance for examination timetabling is a previously proposed strategy known as the choice function.


pdf

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doi

The doi for this publication is 10.1007/s10479-010-0782-2 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 (1.217), 2013 (1.103), 2012 (1.029), 2011 (0.840), 2010 (0.840), 2010 (0.675), 2009 (0.961), 2008 (0.619), 2007 (0.544), 2006 (0.589), 2005 (0.525), 2004 (0.411), 2003 (0.331), 2002 (0.258), 2001 (0.255), 2000 (0.364)

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{bkmo2012, author = {E. K. Burke and G. Kendall and M. Misir and E. \"{O}zcan},
title = {Monte Carlo hyper-heuristics for examination timetabling},
journal = {Annals of Operations Research},
year = {2012},
volume = {196},
pages = {73--90},
number = {1},
abstract = {Automating the neighbourhood selection process in an iterative approach that uses multiple heuristics is not a trivial task. Hyper-heuristics are search methodologies that not only aim to provide a general framework for solving problem instances at different difficulty levels in a given domain, but a key goal is also to extend the level of generality so that different problems from different domains can also be solved. Indeed, a major challenge is to explore how the heuristic design process might be automated. Almost all existing iterative selection hyper-heuristics performing single point search contain two successive stages; heuristic selection and move acceptance. Different operators can be used in either of the stages. Recent studies explore ways of introducing learning mechanisms into the search process for improving the performance of hyper-heuristics. In this study, a broad empirical analysis is performed comparing Monte Carlo based hyper-heuristics for solving capacitated examination timetabling problems. One of these hyper-heuristics is an approach that overlaps two stages and presents them in a single algorithmic body. A learning heuristic selection method (L) operates in harmony with a simulated annealing move acceptance method using reheating (SA) based on some shared variables. Yet, the heuristic selection and move acceptance methods can be separated as the proposed approach respects the common selection hyper-heuristic framework. The experimental results show that simulated annealing with reheating as a hyper-heuristic move acceptance method has significant potential. On the other hand, the learning hyper-heuristic using simulated annealing with reheating move acceptance (LSA) performs poorly due to certain weaknesses, such as the choice of rewarding mechanism and the evaluation of utility values for heuristic selection as compared to some other hyper-heuristics in examination timetabling. Trials with other heuristic selection methods confirm that the best alternative for the simulated annealing with reheating move acceptance for examination timetabling is a previously proposed strategy known as the choice function.},
doi = {10.1007/s10479-010-0782-2},
issn = {0254-5330},
keywords = {Hyper-heuristics, Simulated annealing, Meta-heuristics, Examination timetabling, Reinforcement learning},
owner = {gxk},
timestamp = {2010.10.12},
webpdf = {http://www.graham-kendall.com/papers/bkmo2012.pdf} }