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
The hunt for MH370
http://bit.ly/1DXRLbu

Latest Blog Post

Snooker: Celebrating 40 years at the Crucible

Random Blog Post

Football Prediction: A decision to be made

Publication(s)

Maximising Stadium Attendance: A Case Study of Malaysian Football
http://bit.ly/eFTURu
Learning with imperfections - a multi-agent neural-genetic trading system with differing levels of social learning
http://bit.ly/hBQypU
Tabu Exponential Monte-Carlo with Counter Heuristic for Examination Timetabling.
http://bit.ly/fjry8k
Evolutionary Computation and Games (Invited Review)
http://bit.ly/f6qvUI

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} }