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

General Algebraic Modeling System (GAMS)

Publication(s)

An adaptive multi-population artificial bee colony algorithm for dynamic optimisation problems
http://bit.ly/29btjbV
Evolving Bin Packing Heuristics with Genetic Programming
http://bit.ly/gPl6d2
Mobile Games with Intelligence: a Killer Application?
http://bit.ly/1dhSrHP
Ghost direction detection and other innovations for Ms. Pac-Man
http://bit.ly/hRqET5

Graham Kendall: Details of Requested Publication


Citation

Abuhamdah, A; Ayob, M; Kendall, G and Sabar, N. R Population based Local Search for university course timetabling problems. Applied Intelligence, 40 (1): 44-53, 2014.

Paper ISSN:0924-669X Electronic ISSN: 1573-7497


Abstract

Population based algorithms are generally better at exploring a search space than local search algorithms (i.e. searches based on a single heuristic). However, the limitation of many population based algorithms is in exploiting the search space. We propose a population based Local Search (PB-LS) heuristic that is embedded within a local search algorithm (as a mechanism to exploit the search space). PB-LS employs two operators. The first is applied to a single solution to determine the force between the incumbent solution and the trial current solution (i.e. a single direction force), whilst the second operator is applied to all solutions to determine the force in all directions. The progress of the search is governed by these forces, either in a single direction or in all directions. Our proposed algorithm is able to both diversify and intensify the search more effectively, when compared to other local search and population based algorithms. We use university course timetabling (Socha benchmark datasets) as a test domain. In order to evaluate the effectiveness of PB-LS, we perform a comparison between the performances of PB-LS with other approaches drawn from the scientific literature. Results demonstrate that PB-LS is able to produce statistically significantly higher quality solutions, outperforming many other approaches on the Socha dataset.


pdf

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doi

The doi for this publication is 10.1007/s10489-013-0444-6 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


Journal Rankings


ISI Web of Knowledge Journal Citation Reports

The Web of Knowledge Journal Citation Reports (often known as ISI Impact Factors) help measure how often an article is cited. You can get an introduction to Journal Citation Reports here. Below I have provided the ISI impact factor for the jourrnal in which this article was published. For complete information I have shown the ISI ranking over a number of years, with the latest ranking highlighted.

2012 (1.853), 2011 (0.849), 2010 (0.881), 2009 (0.988), 2008 (0.775), 2007 (0.500), 2006 (0.329), 2005 (0.569), 2004 (0.477), 2003 (0.776), 2002 (0.686), 2001 (0.493), 2000 (0.420)

URL

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Bibtex

@ARTICLE{aaks2014, author = {A. Abuhamdah and M. Ayob and G. Kendall and N. R. Sabar},
title = {Population based Local Search for university course timetabling problems},
journal = {Applied Intelligence},
year = {2014},
volume = {40},
pages = {44--53},
number = {1},
note = {Paper ISSN:0924-669X Electronic ISSN: 1573-7497},
abstract = {Population based algorithms are generally better at exploring a search space than local search algorithms (i.e. searches based on a single heuristic). However, the limitation of many population based algorithms is in exploiting the search space. We propose a population based Local Search (PB-LS) heuristic that is embedded within a local search algorithm (as a mechanism to exploit the search space). PB-LS employs two operators. The first is applied to a single solution to determine the force between the incumbent solution and the trial current solution (i.e. a single direction force), whilst the second operator is applied to all solutions to determine the force in all directions. The progress of the search is governed by these forces, either in a single direction or in all directions. Our proposed algorithm is able to both diversify and intensify the search more effectively, when compared to other local search and population based algorithms. We use university course timetabling (Socha benchmark datasets) as a test domain. In order to evaluate the effectiveness of PB-LS, we perform a comparison between the performances of PB-LS with other approaches drawn from the scientific literature. Results demonstrate that PB-LS is able to produce statistically significantly higher quality solutions, outperforming many other approaches on the Socha dataset.},
doi = {10.1007/s10489-013-0444-6},
issn = {0924-669X},
keywords = {Course timetabling problem, Metaheuristics, Population based algorithm, Hybrid methods, Gravitational emulation},
owner = {Graham},
timestamp = {2013.08.02},
webpdf = {http://www.graham-kendall.com/papers/aaks2014.pdf} }