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 involved with a spin out company that specialises in Strategic Resource Planning
http://bit.ly/eTPZO2
How to teach Deep Blue to play poker and deliver groceries
http://bit.ly/1DXGeZD

Latest Blog Post

How Isaac Newton could help you beat the casino at roulette

Random Blog Post

MISTA Conference: Plenary Talk (Moshe Dror)

Publication(s)

Problem Difficulty and Code Growth in Genetic Programming
http://bit.ly/eTibpi
A Strategy with Novel Evolutionary Features for the Iterated Prisoner's Dilemma
http://bit.ly/eURggX
A multi-agent based simulated stock market - testing on different types of stocks
http://bit.ly/haEt18
A hybrid placement strategy for the three-dimensional strip packing problem
http://bit.ly/fYwujY

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} }