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

How are university examinations scheduled?
http://bit.ly/1z0pG4s
I have published a few papers on Sports Scheduling.
http://bit.ly/gVaUqT

Latest Blog Post

Snooker: Celebrating 40 years at the Crucible

Random Blog Post

Tweeting from PHP

Publication(s)

A Monte Carlo Hyper-Heuristic To Optimise Component Placement Sequencing For Multi Head Placement Machine
http://bit.ly/dJZXFI
Using Harmony Search with Multiple Pitch Adjustment Operators for the Portfolio Selection Problem
http://bit.ly/1okj5eC
Hybridising heuristics within an estimation distribution algorithm for examination timetabling
http://bit.ly/1Plbd56
A Dynamic Multiarmed Bandit-Gene Expression Programming Hyper-Heuristic for Combinatorial Optimization Problems
http://bit.ly/1AfTYKx

Graham Kendall: Details of Requested Publication


Citation

Burke, E. K; Hyde, M; Kendall, G; Ochoa, G; Özcan, E and Woodward, J. R Exploring hyper-heuristic methodologies with genetic programming. In Computational Intelligence: Collaboration, Fusion and Emergence, pages 177-201, Springer, Intelligent Systems Reference Library 1, 2009.


Abstract

Hyper-heuristics represent a novel search methodology that is motivated by the goal of automating the process of selecting or combining simpler heuristics in order to solve hard computational search problems. An extension of the original hyper-heuristic idea is to generate new heuristics which are not currently known. These approaches operate on a search space of heuristics rather than directly on a search space of solutions to the underlying problem which is the case with most meta-heuristics implementations. In the majority of hyper-heuristic studies so far, a framework is provided with a set of human designed heuristics, taken from the literature, and with good measures of performance in practice. A less well studied approach aims to generate new heuristics from a set of potential heuristic components. The purpose of this chapter is to discuss this class of hyper-heuristics, in which Genetic Programming is the most widely used methodology. A detailed discussion is presented including the steps needed to apply this technique, some representative case studies, a literature review of related work, and a discussion of relevant issues. Our aim is to convey the exciting potential of this innovative approach for automating the heuristic design process.


pdf

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doi

The doi for this publication is 10.1007/978-3-642-01799-5_6 You can link directly to the original paper, via the doi, from here

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URL

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Bibtex

@INBOOK{bhkoow2009, chapter = {Computational Intelligence: Collaboration, Fusion and Emergence},
pages = {177--201},
title = {Exploring hyper-heuristic methodologies with genetic programming},
publisher = {Springer},
year = {2009},
editor = {C. L. Mumford and L. C. Jain},
author = {E. K. Burke and M. Hyde and G. Kendall and G. Ochoa and E. Özcan and J. R. Woodward},
volume = {1},
number = {III},
series = {Intelligent Systems Reference Library},
abstract = {Hyper-heuristics represent a novel search methodology that is motivated by the goal of automating the process of selecting or combining simpler heuristics in order to solve hard computational search problems. An extension of the original hyper-heuristic idea is to generate new heuristics which are not currently known. These approaches operate on a search space of heuristics rather than directly on a search space of solutions to the underlying problem which is the case with most meta-heuristics implementations. In the majority of hyper-heuristic studies so far, a framework is provided with a set of human designed heuristics, taken from the literature, and with good measures of performance in practice. A less well studied approach aims to generate new heuristics from a set of potential heuristic components. The purpose of this chapter is to discuss this class of hyper-heuristics, in which Genetic Programming is the most widely used methodology. A detailed discussion is presented including the steps needed to apply this technique, some representative case studies, a literature review of related work, and a discussion of relevant issues. Our aim is to convey the exciting potential of this innovative approach for automating the heuristic design process.},
doi = {10.1007/978-3-642-01799-5_6},
keywords = {genetic programming, hyper-heuristic, hyperheuristic},
owner = {gxk},
timestamp = {2012.01.04},
webpdf = {http://www.graham-kendall.com/papers/bhkoow2009.pdf} }