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 football fixtures worked out?
http://bit.ly/1z0oTAH
Can ants play chess? Yes they can!
http://bit.ly/1yW3UhX

Latest Blog Post

How Isaac Newton could help you beat the casino at roulette

Random Blog Post

Football Prediction: A decision to be made

Publication(s)

Tabu Search Hyper-Heuristic Approach to the Examination Timetabling Problem at University Technology MARA
http://bit.ly/fOXpLD
Hyper-heuristics
http://bit.ly/1a2WNWE
On Nash equilibrium and evolutionarily stable states that are not characterised by the folk theorem
http://bit.ly/1J4KNC0
Scheduling TV Commercials: Models and Solution Methodologies
http://bit.ly/idSBCA

Graham Kendall: Details of Requested Publication


Citation

Ayob, M and Kendall, G A Monte Carlo Hyper-Heuristic To Optimise Component Placement Sequencing For Multi Head Placement Machine. In Proceedings of the International Conference on Intelligent Technologies (InTech'03), pages 132-141, Chiang Mai, Thailand, 2003.


Abstract

In this paper we introduce a Monte Carlo based hyper-heuristic. The Monte Carlo hyper-heuristic manages a set of low level heuristics (in this case just simple 2-opt swaps but they could be any other heuristics). Each of the low level heuristics is responsible for creating a unique neighbour that may be impossible to create by the other low level heuristics. On each iteration, the Monte Carlo hyper heuristic randomly calls a low level heuristic. The new solution returned by the low level heuristic will be accepted based on the Monte Carlo acceptance criteria. The Monte Carlo acceptance criteria always accept an improved solution. Worse solutions will be accepted with a certain probability, which decreases with worse solutions, in order to escape local minima. We develop three hyper-heuristics based on a Monte Carlo method, these being Linear Monte Carlo Exponential Monte Carlo and Exponential Monte Carlo with counter. We also investigate four other hyperheuristics to examine their performance and for comparative purposes. To demonstrate our approach we employ these hyper-heuristics to optimise component placement sequencing in order to improve the efficiency of the multi head placement machine. Experimental results show that the Exponential Monte Carlo hyperheuristic is superior to the other hyper-heuristics and is superior to a choice function hyper-heuristic reported in earlier work.


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Bibtex

@INPROCEEDINGS{ak2003, author = {M. Ayob and G. Kendall},
title = {A Monte Carlo Hyper-Heuristic To Optimise Component Placement Sequencing For Multi Head Placement Machine},
booktitle = {Proceedings of the International Conference on Intelligent Technologies (InTech'03)},
year = {2003},
pages = {132--141},
address = {Chiang Mai, Thailand},
month = {December 17-19},
abstract = {In this paper we introduce a Monte Carlo based hyper-heuristic. The Monte Carlo hyper-heuristic manages a set of low level heuristics (in this case just simple 2-opt swaps but they could be any other heuristics). Each of the low level heuristics is responsible for creating a unique neighbour that may be impossible to create by the other low level heuristics. On each iteration, the Monte Carlo hyper heuristic randomly calls a low level heuristic. The new solution returned by the low level heuristic will be accepted based on the Monte Carlo acceptance criteria. The Monte Carlo acceptance criteria always accept an improved solution. Worse solutions will be accepted with a certain probability, which decreases with worse solutions, in order to escape local minima. We develop three hyper-heuristics based on a Monte Carlo method, these being Linear Monte Carlo Exponential Monte Carlo and Exponential Monte Carlo with counter. We also investigate four other hyperheuristics to examine their performance and for comparative purposes. To demonstrate our approach we employ these hyper-heuristics to optimise component placement sequencing in order to improve the efficiency of the multi head placement machine. Experimental results show that the Exponential Monte Carlo hyperheuristic is superior to the other hyper-heuristics and is superior to a choice function hyper-heuristic reported in earlier work.},
keywords = {Intelligent Search, Heuristic, Printed Circuit Board Assembly, Optimisation, hyper-heuristic, hyperheuristic, component placement},
timestamp = {2007.03.29},
webpdf = {http://www.graham-kendall.com/papers/ak2003.pdf} }