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
How are football fixtures worked out?
http://bit.ly/1z0oTAH

Latest Blog Post

Snooker: Celebrating 40 years at the Crucible

Random Blog Post

PATAT 2010: Googlemaps and Multimaps

Publication(s)

A Heuristic Approach for the Travelling Tournament Problem using Optimal Travelling Salesman Tours
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Investigation of an Adaptive Cribbage Player
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The importance of a piece difference feature to Blondie24
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An ant algorithm hyperheuristic for the project presentation scheduling problem
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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.


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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} }