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

The hunt for MH370
http://bit.ly/1DXRLbu
I have published a few papers on Sports Scheduling.
http://bit.ly/gVaUqT

Latest Blog Post

How Isaac Newton could help you beat the casino at roulette

Random Blog Post

When Sports Rules Go Awry: How TheConversation led to a collaborative paper

Publication(s)

The Scalability of Evolved On Line Bin Packing Heuristics
http://bit.ly/eVBJTd
A hyper-heuristic approach to sequencing by hybridization of DNA sequences
http://bit.ly/1mlNjL6
Academic Timetabling: Linking Research and Practice
http://bit.ly/eroN3m
Regulators as Ďagentsí: power and personality in risk regulation and a role for agent-based simulation
http://bit.ly/evaXWn

Graham Kendall: Details of Requested Publication


Citation

Blazewicz, J; Burke, E. K; Kendall, G; Mruczkiewicz, W; Öguz, C and Swiercz, A A hyper-heuristic approach to sequencing by hybridization of DNA sequences. Annals of Operations Research, 207 (1): 27-41, 2013.


Abstract

In this paper we investigate the use of hyper-heuristic methodologies for predicting DNA sequences. In particular, we utilize Sequencing by Hybridization. We believe that this is the first time that hyper-heuristics have been investigated in this domain. A hyper-heuristic is provided with a set of low-level heuristics and the aim is to decide which heuristic to call at each decision point. We investigate three types of hyper-heuristics. Two of these (simulated annealing and tabu search) draw their inspiration from meta-heuristics. The choice function hyper-heuristic draws its inspiration from reinforcement learning. We utilize two independent sets of low-level heuristics. The first set is based on a previous tabu search method, with the second set being a signicant extension to this basic set, including utilizing a dierent representation and introducing the definition of clusters. The datasets we use comprises two randomly generated datasets and also a publicly available biological dataset. In total, we carried out experiments using 70 different combinations of heuristics, using the three datasets mentioned above and investigating six different hyper-heuristic algorithms. Our results demonstrate the eectiveness of a hyper-heuristic approach to this problem domain. It is necessary to provide a good set of low-level heuristics, which are able to both intensify and diversify the search but this approach has demonstrated very encouraging results on this extremely diffcult and important problem domain.


pdf

You can download the pdf of this publication from here


doi

The doi for this publication is 10.1007/s10479-011-0927-y 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 (1.217), 2013 (1.103), 2012 (1.029), 2011 (0.840), 2010 (0.840), 2010 (0.675), 2009 (0.961), 2008 (0.619), 2007 (0.544), 2006 (0.589), 2005 (0.525), 2004 (0.411), 2003 (0.331), 2002 (0.258), 2001 (0.255), 2000 (0.364)

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{bbkmos2013, author = {J. Blazewicz and E. K. Burke and G. Kendall and W. Mruczkiewicz and C. \"{O}guz and A. Swiercz},
title = {A hyper-heuristic approach to sequencing by hybridization of DNA sequences},
journal = {Annals of Operations Research},
year = {2013},
volume = {207},
pages = {27-41},
number = {1},
abstract = {In this paper we investigate the use of hyper-heuristic methodologies for predicting DNA sequences. In particular, we utilize Sequencing by Hybridization. We believe that this is the first time that hyper-heuristics have been investigated in this domain. A hyper-heuristic is provided with a set of low-level heuristics and the aim is to decide which heuristic to call at each decision point. We investigate three types of hyper-heuristics. Two of these (simulated annealing and tabu search) draw their inspiration from meta-heuristics. The choice function hyper-heuristic draws its inspiration from reinforcement learning. We utilize two independent sets of low-level heuristics. The first set is based on a previous tabu search method, with the second set being a signicant extension to this basic set, including utilizing a dierent representation and introducing the definition of clusters. The datasets we use comprises two randomly generated datasets and also a publicly available biological dataset. In total, we carried out experiments using 70 different combinations of heuristics, using the three datasets mentioned above and investigating six different hyper-heuristic algorithms. Our results demonstrate the eectiveness of a hyper-heuristic approach to this problem domain. It is necessary to provide a good set of low-level heuristics, which are able to both intensify and diversify the search but this approach has demonstrated very encouraging results on this extremely diffcult and important problem domain.},
doi = {10.1007/s10479-011-0927-y},
issn = {0254-5330},
keywords = {hyper-heuristic, hyperheuristic, DNA, Sequencing by Hybridization},
owner = {gxk},
timestamp = {2011.06.22},
webpdf = {http://www.graham-kendall.com/papers/bbkmos2013.pdf} }