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

How are university examinations scheduled?
http://bit.ly/1z0pG4s
I have wriiten a number of articles for TheConversation
http://bit.ly/1yWlOkE

Latest Blog Post

How Isaac Newton could help you beat the casino at roulette

Random Blog Post

Update: Displaying bibtex on web site

Publication(s)

The Cross-domain Heuristic Search Challenge - An International Research Competition
http://bit.ly/1a2VfMs
Introducing a Round Robin Tournament into Evolutionary Individual and Social Learning Checkers
http://bit.ly/1cJw2am
An Ant Based Hyper-heuristic for the Travelling Tournament Problem
http://bit.ly/gPYAJl
Complete and robust no-fit polygon generation for the irregular stock cutting problem
http://bit.ly/fwKSfE

Graham Kendall: Details of Requested Publication


Citation

Burke, E.K; Kendall, G; Misir, M and Ozcan, E A Study of Simulated Annealing Hyperheuristics. In Proceedings of the 7th International Conference on the Practice and Theory of Automated Timetabling (PATAT 2008), 18-22 August 2008, Montreal, Canada, 2008.

This was published in the proceedings as an abstract (not a full paper)


Abstract

One definition of a hyperheuristic is a (meta-)heuristic that carries out a search over the heuristic space formed by a set of low level heuristics (Burke et al., 2003). Hyperheuristics which perturb low level heuristics, utilising a single configuration during the search, are usually iterative methods (Ozcan, Bilgin and Korkmaz, 2006; 2008). At each iteration, the most suitable heuristic (or a subset) is chosen using a heuristic selection method and a new state is generated after the application of the selected heuristic(s). This move is either accepted or rejected based on an acceptance criterion. The process continues until a termination criterion is met. Cowling, Kendall and Soubeiga (2000) proposed a Choice Function (CF) hyperheuristic, with a selection mechanism based on the ideas from reinforcement learning. The choice function maintains a record of the performance of each heuristic. Three criteria are maintained. 1) Its individual performance, 2) how well it has performed with other heuristics and 3) the elapsed time since the heuristic has been called. In Cowling, Kendall and Soubeiga (2002) they utilise an adaptation scheme to adjust the weights of these three components. In both of these studies, simple heuristic selection mechanisms are also described. Simple random (SR) selects one of the heuristics randomly and applies it. A Greedy (GR) strategy applies each low level heuristic to the candidate solution and chooses the one that generates the best change in the objective value.


pdf

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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

@INPROCEEDINGS{bkmo2008, author = {E.K. Burke and G. Kendall and M. Misir and E. Ozcan},
title = {A Study of Simulated Annealing Hyperheuristics},
booktitle = {Proceedings of the 7th International Conference on the Practice and Theory of Automated Timetabling (PATAT 2008)},
year = {2008},
editor = {E.K. Burke and M. Gendreau},
address = {18-22 August 2008, Montreal, Canada},
note = {This was published in the proceedings as an abstract (not a full paper)},
abstract = {One definition of a hyperheuristic is a (meta-)heuristic that carries out a search over the heuristic space formed by a set of low level heuristics (Burke et al., 2003). Hyperheuristics which perturb low level heuristics, utilising a single configuration during the search, are usually iterative methods (Ozcan, Bilgin and Korkmaz, 2006; 2008). At each iteration, the most suitable heuristic (or a subset) is chosen using a heuristic selection method and a new state is generated after the application of the selected heuristic(s). This move is either accepted or rejected based on an acceptance criterion. The process continues until a termination criterion is met. Cowling, Kendall and Soubeiga (2000) proposed a Choice Function (CF) hyperheuristic, with a selection mechanism based on the ideas from reinforcement learning. The choice function maintains a record of the performance of each heuristic. Three criteria are maintained. 1) Its individual performance, 2) how well it has performed with other heuristics and 3) the elapsed time since the heuristic has been called. In Cowling, Kendall and Soubeiga (2002) they utilise an adaptation scheme to adjust the weights of these three components. In both of these studies, simple heuristic selection mechanisms are also described. Simple random (SR) selects one of the heuristics randomly and applies it. A Greedy (GR) strategy applies each low level heuristic to the candidate solution and chooses the one that generates the best change in the objective value.},
keywords = {hyper-heuristics, hyperheuristics, simulated annealing},
owner = {rxj},
timestamp = {2008.09.12},
webpdf = {http://www.graham-kendall.com/papers/bkmo2008.pdf} }