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.

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Publication(s)

A Multi-objective Hyper-heuristic based on Choice Function
http://bit.ly/1f8GQgU
Evidence and belief in regulatory decisions Incorporating expected utility into decision modelling
http://bit.ly/1iaJTKT
The Entity-to-Algorithm Allocation Problem: Extending the Analysis
http://bit.ly/1yHLiyp
Introducing a round robin tournament into Blondie24
http://bit.ly/f4DBrz

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} }