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

If you are interested in hyper-heuristics, take a look at my publications in this area
http://bit.ly/efxLGg
I have published a number of papers on Cutting and Packing
http://bit.ly/dQPw7T

Latest Blog Post

Snooker: Celebrating 40 years at the Crucible

Random Blog Post

P vs NP

Publication(s)

On Nash equilibrium and evolutionarily stable states that are not characterised by the folk theorem
http://bit.ly/1J4KNC0
Throughput Maximization of Queueing Networks with Simultaneous Minimization of Service Rates and Buffers
http://bit.ly/1cJuWLM
Document Zone Classification for Technical Document Images Using Artificial Neural Network and Support Vector Machines
http://bit.ly/1eUn8rs
Does money matter in inflation forecasting?
http://bit.ly/fRebpX

Graham Kendall: Details of Requested Publication


Citation

Kendall, G Applying Meta-Heuristic Algorithms to the Nesting Problem Utilising The No Fit Polygon. Ph.D. Thesis, School of Computer Science, University of Nottingham, UK, 2001.

I started my PhD in September 1997 and graduated in July 2001. My viva took place on 1st December 2000


Abstract

This thesis develops and investigates meta-heuristic and evolutionary approaches to the stock cutting problem. In particular, we concentrate on hill climbing, tabu search, simulated annealing, genetic algorithms, ant algorithms and two types of memetic algorithm. In carrying out this work, a number of diverse issues have had to be explored. It is often the case that the evaluation function is a bottleneck within a search algorithm. This thesis shows that by using a cache to store previously evaluated solutions, the evaluation function does not have to be called when the solution is already in the cache. This significantly improves the speed of the algorithm. However, under certain circumstances, it is beneficial to re-evaluate a solution even though it is stored in the cache. This thesis shows under which conditions this applies and shows that the re-evaluation only has to be done with low probability without affecting solution quality. Ant algorithms are investigated for the first time with regard to stock cutting. This algorithm is relatively new but has been applied successfully to difficult search problems including the travelling salesman problem, vehicle routing and the quadratic assignment problem. In addition, we have implemented and evaluated hybrid ant algorithms with a local search operator. This thesis also develops a no fit polygon algorithm, which is used to pack non-convex shapes. Throughout this thesis both tabu search and a genetic based memetic algorithm consistently produce the best quality results. This is despite trying to find suitable parameters for the other search algorithms so that they carry out more effective searches. The conclusion is that, although tabu search and a genetic based memetic algorithm produce the best quality results, it does not follow that they are the best search strategies for all problems. If there was one area of research that arose from this work it is a need for investigating methods of applying the correct search strategy to any given problem instance.


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Bibtex

@PHDTHESIS{k2001, author = {G. Kendall},
title = {Applying Meta-Heuristic Algorithms to the Nesting Problem Utilising The No Fit Polygon},
school = {School of Computer Science, University of Nottingham, UK},
year = {2001},
type = {PhD},
note = {I started my PhD in September 1997 and graduated in July 2001. My viva took place on 1st December 2000},
abstract = {This thesis develops and investigates meta-heuristic and evolutionary approaches to the stock cutting problem. In particular, we concentrate on hill climbing, tabu search, simulated annealing, genetic algorithms, ant algorithms and two types of memetic algorithm. In carrying out this work, a number of diverse issues have had to be explored. It is often the case that the evaluation function is a bottleneck within a search algorithm. This thesis shows that by using a cache to store previously evaluated solutions, the evaluation function does not have to be called when the solution is already in the cache. This significantly improves the speed of the algorithm. However, under certain circumstances, it is beneficial to re-evaluate a solution even though it is stored in the cache. This thesis shows under which conditions this applies and shows that the re-evaluation only has to be done with low probability without affecting solution quality. Ant algorithms are investigated for the first time with regard to stock cutting. This algorithm is relatively new but has been applied successfully to difficult search problems including the travelling salesman problem, vehicle routing and the quadratic assignment problem. In addition, we have implemented and evaluated hybrid ant algorithms with a local search operator. This thesis also develops a no fit polygon algorithm, which is used to pack non-convex shapes. Throughout this thesis both tabu search and a genetic based memetic algorithm consistently produce the best quality results. This is despite trying to find suitable parameters for the other search algorithms so that they carry out more effective searches. The conclusion is that, although tabu search and a genetic based memetic algorithm produce the best quality results, it does not follow that they are the best search strategies for all problems. If there was one area of research that arose from this work it is a need for investigating methods of applying the correct search strategy to any given problem instance.},
keywords = {Cutting, Packing, Meta-heuristics, No Fit Polygon},
webpdf = {http://www.graham-kendall.com/papers/k2001.pdf} }