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
What do we spend so much in supermarkets?
http://bit.ly/1yW6If7

Latest Blog Post

Snooker: Celebrating 40 years at the Crucible

Random Blog Post

A Day in the Life of Pi

Publication(s)

Co-Evolving Neural networks with Evolutionary Strategies: A New Application to Divisia Money
http://bit.ly/eBV6pc
The Application of a Dendritic Cell Algorithm to a Robotic Classifier
http://bit.ly/hTMQ5K
Finite iterated prisoner's dilemma revisited: belief change and end-game effect
http://bit.ly/hathrT
Automated tile design for self-assembly conformations
http://bit.ly/h7QYiX

Graham Kendall: Details of Requested Publication


Citation

Qu, R; Pham, N; Bai, R and Kendall, G Hybridising heuristics within an estimation distribution algorithm for examination timetabling. Applied Intelligence, 42 (4): 679-693, 2015.


Abstract

This paper presents a hybrid hyper-heuristic approach based on estimation distribution algorithms. The main motivation is to raise the level of generality for search methodologies. The objective of the hyper-heuristic is to produce solutions of acceptable quality for a number of optimisation problems. In this work, we demonstrate the generality through experimental results for different variants of exam timetabling problems. The hyper-heuristic represents an automated constructive method that searches for heuristic choices from a given set of low-level heuristics based only on non-domain-specific knowledge. The high-level search methodology is based on a simple estimation distribution algorithm. It is capable of guiding the search to select appropriate heuristics in different problem solving situations. The probability distribution of low-level heuristics at different stages of solution construction can be used to measure their effectiveness and possibly help to facilitate more intelligent hyper-heuristic search methods.


pdf

You can download the pdf of this publication from here


doi

The doi for this publication is 10.1007/s10489-014-0615-0 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

This pubication does not have a URL associated with it.

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

@ARTICLE{qpbk2015, author = {R. Qu and N. Pham and R. Bai and G. Kendall},
title = {Hybridising heuristics within an estimation distribution algorithm for examination timetabling},
journal = {Applied Intelligence},
year = {2015},
volume = {42},
pages = {679--693},
number = {4},
abstract = {This paper presents a hybrid hyper-heuristic approach based on estimation distribution algorithms. The main motivation is to raise the level of generality for search methodologies. The objective of the hyper-heuristic is to produce solutions of acceptable quality for a number of optimisation problems. In this work, we demonstrate the generality through experimental results for different variants of exam timetabling problems. The hyper-heuristic represents an automated constructive method that searches for heuristic choices from a given set of low-level heuristics based only on non-domain-specific knowledge. The high-level search methodology is based on a simple estimation distribution algorithm. It is capable of guiding the search to select appropriate heuristics in different problem solving situations. The probability distribution of low-level heuristics at different stages of solution construction can be used to measure their effectiveness and possibly help to facilitate more intelligent hyper-heuristic search methods.},
doi = {10.1007/s10489-014-0615-0},
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/qpbk2015.pdf} }