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

Can ants play chess? Yes they can!
http://bit.ly/1yW3UhX
Help solve Santa's logistics problems
http://bit.ly/1DXreuW

Latest Blog Post

Snooker: Celebrating 40 years at the Crucible

Random Blog Post

Bibtax parser: Mashup no more

Publication(s)

Grammatical Evolution Hyper-Heuristic for Combinatorial Optimization Problems
http://bit.ly/19D3Bqv
Hyperheuristics: A Robust Optimisation Method Applied to Nurse Scheduling
http://bit.ly/h17mwh
Population based Monte Carlo tree search hyper-heuristic for combinatorial optimization problems
http://bit.ly/1IIArdQ
An Adaptive Length Chromosome Hyperheuristic Genetic Algorithm for a Trainer Scheduling Problem
http://bit.ly/ezw9NR

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

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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} }