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
How are football fixtures worked out?
http://bit.ly/1z0oTAH

Latest Blog Post

How Isaac Newton could help you beat the casino at roulette

Random Blog Post

Why do researchers not read the aims and scope of a journal?

Publication(s)

A Tabu Search Approach for Graph-Structured Case Retrieval
http://bit.ly/hLtUDZ
The Entity-to-Algorithm Allocation Problem: Extending the Analysis
http://bit.ly/1yHLiyp
The Effects of Extra-Somatic Weapons on the Evolution of Human Cooperation towards Non-Kin
http://bit.ly/1oXDe7O
Towards the 'Decathlon 'Challenge' of search heuristics
http://bit.ly/edfHGs

Graham Kendall: Details of Requested Publication


Citation

Grobler, J; Engelbrecht, A. P; Kendall, G and Yadavalli, V.S.S Heuristic Space Diversity Control for Improved Meta-Hyper-Heuristic Performance. Information Sciences, 300: 49-62, 2015.

ISSN: 0020-0255


Abstract

This paper expands on the concept of heuristic space diversity and investigates various strategies for the management of heuristic space diversity within the context of a metahyper- heuristic algorithm in search of greater performance benefits. Evaluation of various strategies on a diverse set of floating-point benchmark problems shows that heuristic space diversity has a significant impact on hyper-heuristic performance. An exponentially increasing strategy (EIHH) obtained the best results. The value of a priori information about constituent algorithm performance on the benchmark set in question was also evaluated. Finally, EIHH demonstrated good performance when compared to a popular population based algorithm portfolio algorithm and the best performing constituent algorithm.


pdf

You can download the pdf of this publication from here


doi

The doi for this publication is 10.1016/j.ins.2014.11.012 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 (4.038), 2013 (3.893), 2012 (3.643), 2011 (2.833), 2010 (2.836), 2009 (3.291), 2008 (3.095), 2007 (2.147), 2006 (1.003), 2005 (0.723), 2004 (0.540), 2003 (0.447), 2002 (0.361), 2001 (0.264), 2000 (0.322)

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{geky2015, author = {J. Grobler and A. P. Engelbrecht and G. Kendall and V.S.S. Yadavalli},
title = {Heuristic Space Diversity Control for Improved Meta-Hyper-Heuristic Performance},
journal = {Information Sciences},
year = {2015},
volume = {300},
pages = {49--62},
note = {ISSN: 0020-0255},
abstract = {This paper expands on the concept of heuristic space diversity and investigates various strategies for the management of heuristic space diversity within the context of a metahyper- heuristic algorithm in search of greater performance benefits. Evaluation of various strategies on a diverse set of floating-point benchmark problems shows that heuristic space diversity has a significant impact on hyper-heuristic performance. An exponentially increasing strategy (EIHH) obtained the best results. The value of a priori information about constituent algorithm performance on the benchmark set in question was also evaluated. Finally, EIHH demonstrated good performance when compared to a popular population based algorithm portfolio algorithm and the best performing constituent algorithm.},
doi = {10.1016/j.ins.2014.11.012},
issn = {0020-0255},
owner = {Graham},
timestamp = {2014.12.03},
webpdf = {http://www.graham-kendall.com/papers/geky2015.pdf} }