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 blog occasionally, feel free to take a look.
http://bit.ly/hq6rMK
Can ants play chess? Yes they can!
http://bit.ly/1yW3UhX

Latest Blog Post

Snooker: Celebrating 40 years at the Crucible

Random Blog Post

2010 Pac-Man Competition at CIG 2010

Publication(s)

Scripting the Game of Lemmings with a Genetic Algorithm
http://bit.ly/g0igy0
A Puzzle to Challenge Genetic Programming
http://bit.ly/egBVYj
Evaluation of Two Dimensional Bin Packing Problem using the No Fit Polygon
http://bit.ly/dIcplc
Iterated Local Search vs. Hyper-heuristics: Towards General-Purpose Search Algorithms
http://bit.ly/gWFcuw

Graham Kendall: Details of Requested Publication


Citation

Sabar, N.R; Ayob, M and Kendall, G Tabu Exponential Monte-Carlo with Counter Heuristic for Examination Timetabling. In Proceedings of the IEEE Symposium on Computational Intelligence in Scheduling, 2009 (CISched 2009), pages 90-94, 2009.


Abstract

In this work, we introduce a new heuristic TEMCQ (Tabu Exponential Monte-Carlo with Counter) for solving exam timetabling problems. This work, an extension of the EMCQ (Exponential Monte-Carlo with Counter) heuristic that was originally introduced by Ayob and Kendall. EMCQ accepts an improved solution but intelligently accepts worse solutions depending on the solution quality, search time and the number of consecutive non-improving iterations. In order to enhance the EMCQ heuristic, we hybridise it with tabu search, in which the accepted moves are kept in a tabu list for a certain number of iterations in order to avoid cyclic moves. In this work, we test TEMCQ on the un-capacitated Carter's benchmark examination timetable dataset and evaluate the heuristic performance using standard proximity cost. We compare our results against other methodologies that have been reported in the literature over recent years. Results demonstrate that TEMCQ produces good results and outperforms other approaches on several benchmark instances.


pdf

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doi

The doi for this publication is 10.1109/SCIS.2009.4927020 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



URL

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Bibtex

@INPROCEEDINGS{sak2009b, author = {N.R. Sabar and M. Ayob and G. Kendall},
title = {Tabu Exponential Monte-Carlo with Counter Heuristic for Examination Timetabling.},
booktitle = {Proceedings of the IEEE Symposium on Computational Intelligence in Scheduling, 2009 (CISched 2009)},
year = {2009},
pages = {90--94},
month = {30 March - 2nd April 2009},
organization = {Nashville, Tennessee, USA},
abstract = {In this work, we introduce a new heuristic TEMCQ (Tabu Exponential Monte-Carlo with Counter) for solving exam timetabling problems. This work, an extension of the EMCQ (Exponential Monte-Carlo with Counter) heuristic that was originally introduced by Ayob and Kendall. EMCQ accepts an improved solution but intelligently accepts worse solutions depending on the solution quality, search time and the number of consecutive non-improving iterations. In order to enhance the EMCQ heuristic, we hybridise it with tabu search, in which the accepted moves are kept in a tabu list for a certain number of iterations in order to avoid cyclic moves. In this work, we test TEMCQ on the un-capacitated Carter's benchmark examination timetable dataset and evaluate the heuristic performance using standard proximity cost. We compare our results against other methodologies that have been reported in the literature over recent years. Results demonstrate that TEMCQ produces good results and outperforms other approaches on several benchmark instances.},
doi = {10.1109/SCIS.2009.4927020},
keywords = {tabu search, monte carlo, examination timetabling, EMCQ, heuristic, meta-heuristics},
owner = {est},
timestamp = {2010.03.18},
webpdf = {http://www.graham-kendall.com/papers/sak2009b.pdf} }