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
How are university examinations scheduled?
http://bit.ly/1z0pG4s

Latest Blog Post

Snooker: Celebrating 40 years at the Crucible

Random Blog Post

What is Operations Research?

Publication(s)

Automating the Packing Heuristic Design Process with Genetic Programming
http://bit.ly/19OfB8C
Managing University-Industry Collaborations in Malaysia by Examining its Critical Success Factors: A Dyadic Approach
http://bit.ly/2df4YZ5
Journals Rankings: Buyer Beware
http://bit.ly/1iaSVYu
On Nie-Tan operator and type-reduction of interval type-2 fuzzy sets
http://bit.ly/2kqxtD3

Graham Kendall: Details of Requested Publication


Citation

Bai, R; Burke, E.K; Gendreau, M; Kendall, G and McCollum, B Memory Length in Hyper-heuristics: An Empirical Study. In Proceedings of the 2007 IEEE Symposium on Computational Intelligence in Scheduling (CISched2007), pages 173-178, 2007.


Abstract

Hyper-heuristics are an emergent optimisation methodology which aims to give a higher level of flexibility and domain-independence than is currently possible. Hyper-heuristics are able to adapt to the different problems or problem instances by dynamically choosing between heuristics during the search. This paper is concerned with the issues of memory length on the performance of hyper-heuristics. We focus on a recently proposed simulated annealing hyper-heuristic and choose a set of hard university course timetabling problems as the test bed for this empirical study. The experimental results show that the memory length can affect the performance of hyper-heuristics and a good choice of memory length is able to improve solution quality. Finally, two dynamic approaches are investigated and one of the approaches is shown to be able to produce promising results without introducing extra sensitive algorithmic parameters.


pdf

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doi

The doi for this publication is 10.1109/SCIS.2007.367686 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{bbkm2007, author = {R. Bai and E.K. Burke and M. Gendreau and G. Kendall and B. McCollum},
title = {Memory Length in Hyper-heuristics: An Empirical Study},
booktitle = {Proceedings of the 2007 IEEE Symposium on Computational Intelligence in Scheduling (CISched2007)},
year = {2007},
pages = {173--178},
month = {1-5 April},
organization = {Hilton Hawaiian Village, Honolulu, Hawaii, USA},
abstract = {Hyper-heuristics are an emergent optimisation methodology which aims to give a higher level of flexibility and domain-independence than is currently possible. Hyper-heuristics are able to adapt to the different problems or problem instances by dynamically choosing between heuristics during the search. This paper is concerned with the issues of memory length on the performance of hyper-heuristics. We focus on a recently proposed simulated annealing hyper-heuristic and choose a set of hard university course timetabling problems as the test bed for this empirical study. The experimental results show that the memory length can affect the performance of hyper-heuristics and a good choice of memory length is able to improve solution quality. Finally, two dynamic approaches are investigated and one of the approaches is shown to be able to produce promising results without introducing extra sensitive algorithmic parameters.},
doi = {10.1109/SCIS.2007.367686},
keywords = {hyper-heuristics, hyperheuristics, simulated annealing, timetabling, memory},
timestamp = {2007.06.11},
webpdf = {http://www.graham-kendall.com/papers/bbkm2007.pdf} }