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

I blog occasionally, feel free to take a look.
http://bit.ly/hq6rMK
The hunt for MH370
http://bit.ly/1DXRLbu

Latest Blog Post

Snooker: Celebrating 40 years at the Crucible

Random Blog Post

Bibtex is not the only solution apparently

Publication(s)

A Hyperheuristic Approach to Scheduling a Sales Summit
http://bit.ly/f5PRaP
Irregular Packing using the Line and Arc No-Fit Polygon
http://bit.ly/hf6IdA
Comparison of meta-heuristic algorithms for clustering rectangles
http://bit.ly/eQQ0Kd
The Application of a Dendritic Cell Algorithm to a Robotic Classifier
http://bit.ly/hTMQ5K

Graham Kendall: Details of Requested Publication


Citation

Burke, E.K and Kendall, G Applying Ant Algorithms and the No Fit Polygon to the Nesting Problem. In Proceedings of the 12th Australian Joint Conference on Artificial Intelligence (AIí99), pages 453-464, Sydney, Australia, 6-10 December, Lecture Notes in Artifcial Intelligence 1747, 1999.


Abstract

In previous work solutions for the nesting problem are produced using the no fit polygon (NFP), a new evaluation method and three evolutionary algorithms (simulated annealing (SA), tabu search (TS) and genetic algorithms (GA)). Tabu search has been shown to produce the best quality solutions for two problems. In this paper this work is developed. A relatively new type of search algorithm (ant algorithm) is developed and the results from this algorithm are compared against SA, TS and GA We discuss the ideas behind ant algorithms and describe how they have been implemented with regards to the nesting problem. The evaluation method used is described, as is the NFP. Computational results are given.


pdf

You can download the pdf of this publication from here


doi

The doi for this publication is 10.1007/3-540-46695-9_38 You can link directly to the original paper, via the doi, from here

What is a doi?: A doi (Document Object Identifier) is a unique identifier for sicientific papers (and occasionally other material). This provides direct access to the location where the original article is published using the URL http://dx.doi/org/xxxx (replacing xxx with the doi). See http://dx.doi.org/ for more information



URL

The URL for additional information is http://dx.doi.org/10.1007/3-540-46695-9

The URL is only provided if there is additional information that might be useful. For example, where the entry is a book chapter, the URL might link to the book itself.


Bibtex

@INPROCEEDINGS{bk1999a, author = {E.K. Burke and G. Kendall},
title = {Applying Ant Algorithms and the No Fit Polygon to the Nesting Problem},
booktitle = {Proceedings of the 12th Australian Joint Conference on Artificial Intelligence (AIí99)},
year = {1999},
editor = {N. Foo},
volume = {1747},
series = {Lecture Notes in Artifcial Intelligence},
pages = {453--464},
address = {Sydney, Australia, 6-10 December},
abstract = {In previous work solutions for the nesting problem are produced using the no fit polygon (NFP), a new evaluation method and three evolutionary algorithms (simulated annealing (SA), tabu search (TS) and genetic algorithms (GA)). Tabu search has been shown to produce the best quality solutions for two problems. In this paper this work is developed. A relatively new type of search algorithm (ant algorithm) is developed and the results from this algorithm are compared against SA, TS and GA We discuss the ideas behind ant algorithms and describe how they have been implemented with regards to the nesting problem. The evaluation method used is described, as is the NFP. Computational results are given.},
comment = {ISBN 1-892512-79-3},
doi = {10.1007/3-540-46695-9_38},
keywords = {packing, nesting, no fit polygon, NFP, ant algorithms, ACO},
timestamp = {2007.03.29},
url = {http://dx.doi.org/10.1007/3-540-46695-9},
webpdf = {http://www.graham-kendall.com/papers/bk1999a.pdf} }