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

Does AI have a place in the board room?
http://bit.ly/1DXreuW
Can ants play chess? Yes they can!
http://bit.ly/1yW3UhX

Latest Blog Post

Snooker: Celebrating 40 years at the Crucible

Random Blog Post

Can the Traveling Salesman Problem help Santa deliver presents?

Publication(s)

A Hybrid Evolutionary Approach to the Nurse Rostering Problem
http://bit.ly/ey147Y
A Variable Neighborhood Descent Search Algorithm for Delay-Constrained Least-Cost Multicast Routing
http://bit.ly/h1puUB
An artificial neural network for predicting domestic hot water characteristics
http://bit.ly/dNtSFu
Towards the 'Decathlon 'Challenge' of search heuristics
http://bit.ly/edfHGs

Graham Kendall: Details of Requested Publication


Citation

Hingston, P and Kendall, G Ant Colonies Discover Knight's Tours. In Proceedings of Advances in Artificial Intelligence: 17th Australian Joint Conference on Artificial Intelligence (AI'04), pages 1213-1218, Cairns, Australia, Lecture Notes in Computer Science 3339, 2005.


Abstract

In this paper we introduce an Ant Colony Optimisation (ACO) algorithm to find solutions for the well-known Knightrsquos Tour problem. The algorithm utilizes the implicit parallelism of ACOrsquos to simultaneously search for tours starting from all positions on the chessboard. We compare the new algorithm to a recently reported genetic algorithm, and to a depth-first backtracking search using Warnsdorffrsquos heuristic. The new algorithm is superior in terms of search bias and also in terms of the rate of finding solutions.


pdf

You can download the pdf of this publication from here


doi

The doi for this publication is 10.1007/978-3-540-30549-1_125 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/b104336

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{hk2005a, author = {P. Hingston and G. Kendall},
title = {Ant Colonies Discover Knight's Tours},
booktitle = {Proceedings of Advances in Artificial Intelligence: 17th Australian Joint Conference on Artificial Intelligence (AI'04)},
year = {2005},
editor = {G.I. Webb and X. Yu},
volume = {3339},
series = {Lecture Notes in Computer Science},
pages = {1213--1218},
address = {Cairns, Australia},
month = {December 4-6},
abstract = {In this paper we introduce an Ant Colony Optimisation (ACO) algorithm to find solutions for the well-known Knightrsquos Tour problem. The algorithm utilizes the implicit parallelism of ACOrsquos to simultaneously search for tours starting from all positions on the chessboard. We compare the new algorithm to a recently reported genetic algorithm, and to a depth-first backtracking search using Warnsdorffrsquos heuristic. The new algorithm is superior in terms of search bias and also in terms of the rate of finding solutions.},
comment = {ISBN: 3-540-24059-4, ISSN: 0302-9743},
doi = {10.1007/978-3-540-30549-1_125},
keywords = {games, chess, knight's tour, knights tournament, ACO, ant colony optimisation},
timestamp = {2007.03.29},
url = {http://dx.doi.org/10.1007/b104336},
webpdf = {http://www.graham-kendall.com/papers/hk2005a.pdf} }