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 have published a number of papers on Cutting and Packing
http://bit.ly/dQPw7T
A Conversation article celebrating Pi
http://bit.ly/1DXuXbV

Latest Blog Post

Snooker: Celebrating 40 years at the Crucible

Random Blog Post

Random Number Generation

Publication(s)

Collective Behavior and Kin Selection in Evolutionary IPD
http://bit.ly/if34nF
Backward Induction and Repeated Games With Strategy Constraints: An Inspiration From the Surprise Exam Paradox
http://bit.ly/1ib50Nd
Evolutionary Strategies vs. Neural Networks; New Evidence from Taiwan on the Divisia Index Debate
http://bit.ly/iiwrjD
Chapter 4: Genetic Algorithms
http://bit.ly/1h1JBCi

Graham Kendall: Details of Requested Publication


Citation

Swan, J; Özcan, E and Kendall, G Hyperion - A recursive hyperheuristic framework. In Proceedings of Learning and Intelligent Optmization (LION5), pages 610-630, Lectures Notes in Computer Science 6683, 2011.


Abstract

Hyper-heuristics are methodologies used to search the space of heuristics for solving computationally dicult problems. We describe an object-oriented domain analysis for hyper-heuristics that orthogonally decomposes the domain into generative policy components. The framework facilitates the recursive instantiation of hyper-heuristics over hyper-heuristics, allowing further exploration of the possibilities implied by the hyper-heuristic concept. We describe Hyperion, a JavaTM class library implementation of this domain analysis.


pdf

You can download the pdf of this publication from here


doi

The doi for this publication is 10.1007/978-3-642-25566-3_48 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://www.springer.com/computer/theoretical+computer+science/book/978-3-642-25565-6

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

@INPROCEEDINGS{sok2011, author = {J. Swan and E. \"{O}zcan and G. Kendall},
title = {Hyperion - A recursive hyperheuristic framework},
booktitle = {Proceedings of Learning and Intelligent Optmization (LION5)},
year = {2011},
editor = {X. Yao and C. A. C. Coello},
volume = {6683},
series = {Lectures Notes in Computer Science},
pages = {610--630},
month = {Jan 17-21, 2011, Rome, Italy},
abstract = {Hyper-heuristics are methodologies used to search the space of heuristics for solving computationally dicult problems. We describe an object-oriented domain analysis for hyper-heuristics that orthogonally decomposes the domain into generative policy components. The framework facilitates the recursive instantiation of hyper-heuristics over hyper-heuristics, allowing further exploration of the possibilities implied by the hyper-heuristic concept. We describe Hyperion, a JavaTM class library implementation of this domain analysis.},
doi = {10.1007/978-3-642-25566-3_48},
keywords = {hyper-heuristics, hyperheuristics, Hyperion, framework, simulated annealing, great deluge, exponential monte carlo, reinforcement learning, tabu search, evolution strategy, ant algorithm, SAT, boolean satisfiability},
owner = {gxk},
timestamp = {2010.12.09},
url = {http://www.springer.com/computer/theoretical+computer+science/book/978-3-642-25565-6},
webpdf = {http://www.graham-kendall.com/papers/sok2011.pdf} }