Graham Kendall
Various Images

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

Can ants play chess? Yes they can!
http://bit.ly/1yW3UhX
I am a member of the Automated Scheduling, Optimisation and Planning Research Group
http://bit.ly/eIQ5XC

Latest Blog Post

Snooker: Celebrating 40 years at the Crucible

Random Blog Post

What Java GUI development tool shoud I use?

Publication(s)

Alternative hyper-heuristic strategies for multi-method global optimization
http://bit.ly/g1xcMp
General Video Game Playing
http://bit.ly/1a2Vjvz
Evolutionary Strategies - A New Macroeconomic Policy Tool?
http://bit.ly/gor2a0
Evidence and belief in regulatory decisions Incorporating expected utility into decision modelling
http://bit.ly/1iaJTKT

Graham Kendall: Details of Requested Publication


Citation

Burke, E. K; Gendreau, M; Hyde, M; Kendall, G; Ochoa, G; Özcan, E and Qu, R Hyper-heuristics: a survey of the state of the art. Journal of the Operational Research Society, 64 (12): 1695-1724, 2013.


Abstract

Hyper-heuristics comprise a set of approaches that are motivated (at least in part) by the goal of automating the design of heuristic methods to solve hard computational search problems. An underlying strategic research challenge is to develop more generally applicable search methodologies. The term hyper-heuristic is relatively new; it was first used in 2000 to describe heuristics to choose heuristics in the context of combinatorial optimisation. However, the idea of automating the design of heuristics is not new; it can be traced back to the 1960s. The definition of hyper-heuristics has been recently extended to refer to a search method or learning mechanism for selecting or generating heuristics to solve computational search problems. Two main hyper-heuristic categories can be considered: heuristic selection and heuristic generation. The distinguishing feature of hyper-heuristics is that they operate on a search space of heuristics (or heuristic components) rather than directly on the search space of solutions to the underlying problem that is being addressed. This paper presents a critical discussion of the scientific literature on hyper-heuristics including their origin and intellectual roots, a detailed account of the main types of approaches, and an overview of some related areas. Current research trends and directions for future research are also discussed.


pdf

You can download the pdf of this publication from here


doi

The doi for this publication is 10.1057/jors.2013.71 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 (0.953), 2013 (0.911), 2012 (0.989), 2011 (0.971), 2010 (1.102), 2009 (1.009), 2008 (0.839), 2007 (0.784), 2006 (0.597), 2005 (0.603), 2004 (0.515), 2003 (0.416), 2002 (0.493), 2001 (0.438), 2000 (0.648)

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{bghkooq2013, author = {E. K. Burke and M. Gendreau and M. Hyde and G. Kendall and G. Ochoa and E. \"{O}zcan and R. Qu},
title = {Hyper-heuristics: a survey of the state of the art},
journal = {Journal of the Operational Research Society},
year = {2013},
volume = {64},
pages = {1695--1724},
number = {12},
abstract = {Hyper-heuristics comprise a set of approaches that are motivated (at least in part) by the goal of automating the design of heuristic methods to solve hard computational search problems. An underlying strategic research challenge is to develop more generally applicable search methodologies. The term hyper-heuristic is relatively new; it was first used in 2000 to describe heuristics to choose heuristics in the context of combinatorial optimisation. However, the idea of automating the design of heuristics is not new; it can be traced back to the 1960s. The definition of hyper-heuristics has been recently extended to refer to a search method or learning mechanism for selecting or generating heuristics to solve computational search problems. Two main hyper-heuristic categories can be considered: heuristic selection and heuristic generation. The distinguishing feature of hyper-heuristics is that they operate on a search space of heuristics (or heuristic components) rather than directly on the search space of solutions to the underlying problem that is being addressed. This paper presents a critical discussion of the scientific literature on hyper-heuristics including their origin and intellectual roots, a detailed account of the main types of approaches, and an overview of some related areas. Current research trends and directions for future research are also discussed.},
doi = {10.1057/jors.2013.71},
issn = {0160-5682},
owner = {Graham},
timestamp = {2013.07.28},
webpdf = {http://www.graham-kendall.com/papers/bghkooq2013.pdf} }