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

How are university examinations scheduled?
http://bit.ly/1z0pG4s
I blog occasionally, feel free to take a look.
http://bit.ly/hq6rMK

Latest Blog Post

Snooker: Celebrating 40 years at the Crucible

Random Blog Post

MISTA Conference: Plenary Talk (Moshe Dror)

Publication(s)

A Dynamic Multiarmed Bandit-Gene Expression Programming Hyper-Heuristic for Combinatorial Optimization Problems
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We should be just a number, and we should embrace it
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Artificial and Computational Intelligence for Games on Mobile Platforms
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A Dynamic Multiarmed Bandit-Gene Expression Programming Hyper-Heuristic for Combinatorial Optimization Problems
RATE_LIMIT_EXCEEDED

Graham Kendall: Details of Requested Publication


Citation

Soria-Alcaraza, J. A; Özcan, E; Swan, J; Kendall, G and Carpio, M Iterated Local Search Using an Add and Delete Hyper-heuristic for University Course Timetabling. Applied Soft Computing, 40: 581-593, 2016.


Abstract

Hyper-heuristics are (meta-)heuristics that operate at a higher level to choose or generate a set of low-level (meta-)heuristics in an attempt of solve difficult optimization problems. Iterated local search (ILS) is a well-known approach for discrete optimization, combining perturbation and hill-climbing within an iterative framework. In this study, we introduce an ILS approach, strengthened by a hyper-heuristic which generates heuristics based on a fixed number of add and delete operations. The performance of the proposed hyper-heuristic is tested across two different problem domains using real world benchmark of course timetabling instances from the second International Timetabling Competition Tracks 2 and 3. The results show that mixing add and delete operations within an ILS framework yields an effective hyper-heuristic approach.


pdf

You can download the pdf of this publication from here


doi

The doi for this publication is 10.1016/j.asoc.2015.11.043 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.

2014 (2.810), 2013 (2.679), 2012 (2.526), 2011 (2.612), 2010 (2.097), 2009 (2.415), 2008 (1.909), 2007 (1.537), 2006 (0.849)

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{soskc2016, author = {J. A. Soria-Alcaraza and E. \"{O}zcan and J. Swan and G. Kendall and M. Carpio},
title = {Iterated Local Search Using an Add and Delete Hyper-heuristic for University Course Timetabling},
journal = {Applied Soft Computing},
year = {2016},
volume = {40},
pages = {581--593},
abstract = {Hyper-heuristics are (meta-)heuristics that operate at a higher level to choose or generate a set of low-level (meta-)heuristics in an attempt of solve difficult optimization problems. Iterated local search (ILS) is a well-known approach for discrete optimization, combining perturbation and hill-climbing within an iterative framework. In this study, we introduce an ILS approach, strengthened by a hyper-heuristic which generates heuristics based on a fixed number of add and delete operations. The performance of the proposed hyper-heuristic is tested across two different problem domains using real world benchmark of course timetabling instances from the second International Timetabling Competition Tracks 2 and 3. The results show that mixing add and delete operations within an ILS framework yields an effective hyper-heuristic approach.},
doi = {10.1016/j.asoc.2015.11.043},
issn = {1568-4946},
owner = {Graham},
timestamp = {2015.12.13},
webpdf = {http://www.graham-kendall.com/papers/soskc2016.pdf} }