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

The hunt for MH370
http://bit.ly/1DXRLbu
How to teach Deep Blue to play poker and deliver groceries
http://bit.ly/1DXGeZD

Latest Blog Post

Snooker: Celebrating 40 years at the Crucible

Random Blog Post

Improving/targeting my Twitter Feed

Publication(s)

Iterated Local Search Using an Add and Delete Hyper-heuristic for University Course Timetabling
http://bit.ly/1mlRZo4
A New Approach to Packing Non-Convex Polygons Using the No Fit Polygon and Meta-Heuristic and Evolutionary Algorithms
http://bit.ly/eMYCKs
Evolving Neural Networks with Evolutionary Strategies: A New Application to Divisa Money
http://bit.ly/dKzEAy
Solving Multi-objective Optimisation Problems Using the Potential Pareto Regions Evolutionary Algorithm
http://bit.ly/fCOMDK

Graham Kendall: Details of Requested Publication


Citation

Burke, E.K and Kendall, G Evaluation of Two Dimensional Bin Packing Problem using the No Fit Polygon. In Proceedings of the 26th International Conference on Computers and Industrial Engineering, pages 286-291, 15-17 December 1999, Melbourne, Australia, 1999.


Abstract

When employing evolutionary algorithms it is often the case that the evaluation function is the most computationally expensive part of the algorithm. Our evaluation function calculates the no fit polygon (NFP) for two polygons and then calculates the smallest convex hull for these two polygons. This process is repeated for each polygon. As the manipulation of polygons is computational expensive, the algorithm shows a bottleneck at this stage. However, many of the evaluations are simply reevaluating solutions that have already been evaluated. In order to use this information we use a cache which stores previous evaluations. By increasing the size of the cache size the speed of the algorithm is significantly increased. In addition the concept of a polygon type allows much better use to be made of the cache. In some circumstances, it may not be beneficial to use a cached evaluation. A reevaluation parameter is introduced which forces a complete reevaluation of a solution. We show that this parameter can be set to a small value so that we do not lose the advantages of the cache. These approaches are intuitive but are not often implemented, but they will become increasingly important as evolutionary algorithms are more widely used.


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Bibtex

@INPROCEEDINGS{bk1999b, author = {E.K. Burke and G. Kendall},
title = {Evaluation of Two Dimensional Bin Packing Problem using the No Fit Polygon},
booktitle = {Proceedings of the 26th International Conference on Computers and Industrial Engineering},
year = {1999},
pages = {286--291},
address = {15-17 December 1999, Melbourne, Australia},
abstract = {When employing evolutionary algorithms it is often the case that the evaluation function is the most computationally expensive part of the algorithm. Our evaluation function calculates the no fit polygon (NFP) for two polygons and then calculates the smallest convex hull for these two polygons. This process is repeated for each polygon. As the manipulation of polygons is computational expensive, the algorithm shows a bottleneck at this stage. However, many of the evaluations are simply reevaluating solutions that have already been evaluated. In order to use this information we use a cache which stores previous evaluations. By increasing the size of the cache size the speed of the algorithm is significantly increased. In addition the concept of a polygon type allows much better use to be made of the cache. In some circumstances, it may not be beneficial to use a cached evaluation. A reevaluation parameter is introduced which forces a complete reevaluation of a solution. We show that this parameter can be set to a small value so that we do not lose the advantages of the cache. These approaches are intuitive but are not often implemented, but they will become increasingly important as evolutionary algorithms are more widely used.},
issn = {0360-8352},
journal = {Computers \& Industrial Engineering},
keywords = {packing, nesting, evolutionary computation, no fit polygon, NFP, cache},
timestamp = {2007.03.29},
webpdf = {http://www.graham-kendall.com/papers/bk1999b.pdf} }