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

Does AI have a place in the board room?
http://bit.ly/1DXreuW
I am involved with a spin out company that specialises in Strategic Resource Planning
http://bit.ly/eTPZO2

Latest Blog Post

Snooker: Celebrating 40 years at the Crucible

Random Blog Post

Learning Java, the first steps

Publication(s)

Mobile Games with Intelligence: a Killer Application?
http://bit.ly/1dhSrHP
Journals Rankings: Buyer Beware
http://bit.ly/1iaSVYu
Tabu Exponential Monte-Carlo with Counter Heuristic for Examination Timetabling.
http://bit.ly/fjry8k
On Nash equilibrium and evolutionarily stable states that are not characterised by the folk theorem
http://bit.ly/1J4KNC0

Graham Kendall: Details of Requested Publication


Citation

Burke, E.K; Hart, E; Kendall, G; Newall, J; Ross, P and Schulenburg, S Hyper-Heuristics: An Emerging Direction in Modern Search Technology. In Handbook of Meta-Heuristics, pages 457-474, Kluwer, 2003.


Abstract

This chapter introduces and overviews an emerging methodology in search and optimisation. One of the key aims of these new approaches, which have been termed hyper-heuristics, is to raise the level of generality at which optimisation systems can operate. An objective is that hyper-heuristics will lead to more general systems that are able to handle a wide range of problem domains rather than current meta-heuristic technology which tends to be customised to a particular problem or a narrow class of problems. Hyperheuristics are broadly concerned with intelligently choosing the right heuristic or algorithm in a given situation. Of course, a hyper-heuristic can be (often is) a (meta-)heuristic and it can operate on (meta-)heuristics. In a certain sense, a hyper-heuristic works at a higher level when compared with the typical application of meta-heuristics to optimisation problems i.e. a hyper-heuristic could be thought of as a (meta)-heuristic which operates on lower level (meta-)heuristics. In this chapter we will introduce the idea and give a brief history of this emerging area. In addition, we will review some of the latest work to be published in the field.


pdf

You can download the pdf of this publication from here


doi

The doi for this publication is 10.1007/0-306-48056-5_16 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



URL

The URL for additional information is http://dx.doi.org/10.1007/b101874

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

@INBOOK{bhknrs2003, chapter = {Handbook of Meta-Heuristics},
pages = {457--474},
title = {Hyper-Heuristics: An Emerging Direction in Modern Search Technology},
publisher = {Kluwer},
year = {2003},
editor = {F. Glover and G. Kochenberger},
author = {E.K. Burke and E. Hart and G. Kendall and J. Newall and P. Ross and S. Schulenburg},
abstract = {This chapter introduces and overviews an emerging methodology in search and optimisation. One of the key aims of these new approaches, which have been termed hyper-heuristics, is to raise the level of generality at which optimisation systems can operate. An objective is that hyper-heuristics will lead to more general systems that are able to handle a wide range of problem domains rather than current meta-heuristic technology which tends to be customised to a particular problem or a narrow class of problems. Hyperheuristics are broadly concerned with intelligently choosing the right heuristic or algorithm in a given situation. Of course, a hyper-heuristic can be (often is) a (meta-)heuristic and it can operate on (meta-)heuristics. In a certain sense, a hyper-heuristic works at a higher level when compared with the typical application of meta-heuristics to optimisation problems i.e. a hyper-heuristic could be thought of as a (meta)-heuristic which operates on lower level (meta-)heuristics. In this chapter we will introduce the idea and give a brief history of this emerging area. In addition, we will review some of the latest work to be published in the field.},
doi = {10.1007/0-306-48056-5_16},
keywords = {search, optimisation, optimization, hyper-heuristics, hyperheuristics, meta-heuristics, metaheuristics, heuristics},
url = {http://dx.doi.org/10.1007/b101874},
webpdf = {http://www.graham-kendall.com/papers/bhknrs2003.pdf} }