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

I am a member of the Automated Scheduling, Optimisation and Planning Research Group
http://bit.ly/eIQ5XC
How to teach Deep Blue to play poker and deliver groceries
http://bit.ly/1DXGeZD

Latest Blog Post

Snooker: Celebrating 40 years at the Crucible

Random Blog Post

Latex Editors: WinEdt versus TeXstudio

Publication(s)

A Study of Simulated Annealing Hyperheuristics
http://bit.ly/ifevCO
Iterated Local Search vs. Hyper-heuristics: Towards General-Purpose Search Algorithms
http://bit.ly/gWFcuw
Hybrid Heuristic for Multi-carrier Transportation Plans
http://bit.ly/1dGGwqO
On Nie-Tan operator and type-reduction of interval type-2 fuzzy sets
http://bit.ly/2kqxtD3

Graham Kendall: Details of Requested Publication


Citation

Kendall, G and Burke, E.K Hyper-heuristics. In Wiley Encyclopedia of Operations Research and Management Science, Wiley, 2011.


Abstract

This article provides an introduction to hyperheuristics, the origins of which can be traced back to the 1960s. There was a significant increase in research interest from about 2000. Hyperheuristics can be seen as either heuristics to choose heuristics or as heuristics to generate heuristics. In contrast to most uses of metaheuristics, which usually search the solution space directly, hyperheuristics search over a heuristic space. One motivation of the area is that, by operating in this way it might be possible to raise the level of generality of search algorithms so that they can address a broader spectrum of problems (without human adaptation) than is possible today. Another long-term aim of hyperheuristic research is to build search methodologies that are able to automatically adapt themselves to different problem instances/domains with as little human input as possible. This article provides access to relevant literature, in addition to providing a brief history of the area and describing the two approaches (selection of heuristics and generating heuristics) mentioned above. It is intended as a brief and initial introduction to the area.


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doi

The doi for this publication is 10.1002/9780470400531.eorms0391 You can link directly to the original paper, via the doi, from here

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URL

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Bibtex

@INBOOK{kb2011, chapter = {Wiley Encyclopedia of Operations Research and Management Science},
title = {Hyper-heuristics},
publisher = {Wiley},
year = {2011},
author = {G. Kendall and E.K. Burke},
abstract = {This article provides an introduction to hyperheuristics, the origins of which can be traced back to the 1960s. There was a significant increase in research interest from about 2000. Hyperheuristics can be seen as either heuristics to choose heuristics or as heuristics to generate heuristics. In contrast to most uses of metaheuristics, which usually search the solution space directly, hyperheuristics search over a heuristic space. One motivation of the area is that, by operating in this way it might be possible to raise the level of generality of search algorithms so that they can address a broader spectrum of problems (without human adaptation) than is possible today. Another long-term aim of hyperheuristic research is to build search methodologies that are able to automatically adapt themselves to different problem instances/domains with as little human input as possible. This article provides access to relevant literature, in addition to providing a brief history of the area and describing the two approaches (selection of heuristics and generating heuristics) mentioned above. It is intended as a brief and initial introduction to the area.},
doi = {10.1002/9780470400531.eorms0391},
keywords = {hyper-heuristic, hyperheuristic},
owner = {gxk},
timestamp = {2011.03.12},
url = {http://dx.doi.org/10.1002/9780470400531} }