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
If you are interested in hyper-heuristics, take a look at my publications in this area
http://bit.ly/efxLGg

Latest Blog Post

Snooker: Celebrating 40 years at the Crucible

Random Blog Post

MISTA Conference: Venue for 2011 announced

Publication(s)

An efficient and robust approach to generate high quality solutions for the Traveling Tournament Problem.
RATE_LIMIT_EXCEEDED
Computing Nash Equilibria and Evolutionarily Stable States of Evolutionary Games
RATE_LIMIT_EXCEEDED
Backward Induction and Repeated Games With Strategy Constraints: An Inspiration From the Surprise Exam Paradox
RATE_LIMIT_EXCEEDED
Effect of Look-Ahead Depth in Evolutionary Checkers
RATE_LIMIT_EXCEEDED

Graham Kendall: Details of Requested Publication


Citation

Burke, E.K; Hyde, M; Kendall, G; Ochoa, G; Ozcan, E and Woodward, J. R A Classification of Hyper-heuristic Approaches. In Handbook of Meta-Heuristics, pages 449-468, Kluwer, 2010.


Abstract

The current state of the art in hyper-heuristic research comprises a set of approaches that share the common goal of automating the design and adaptation of heuristic methods to solve hard computational search problems. The main goal is to produce more generally applicable search methodologies. In this chapter we present an overview of previous categorisations of hyper-heuristics and provide a unified classification and definition, which capture the work that is being undertaken in this field. We distinguish between two main hyper-heuristic categories: heuristic selection and heuristic generation. Some representative examples of each category are discussed in detail. Our goals are to clarify the mainfeatures of existing techniques and to suggest new directions for hyper-heuristic research.


pdf

You can download the pdf of this publication from here


doi

The doi for this publication is 10.1007/978-1-4419-1665-5_15 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/10.1007/978-1-4419-1665-5

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{bhkoow2010, chapter = {Handbook of Meta-Heuristics},
pages = {449--468},
title = {A Classification of Hyper-heuristic Approaches},
publisher = {Kluwer},
year = {2010},
editor = {M. Gendreau and J-Y. Potvin},
author = {E.K. Burke and M. Hyde and G. Kendall and G. Ochoa and E. Ozcan and J. R. Woodward},
abstract = {The current state of the art in hyper-heuristic research comprises a set of approaches that share the common goal of automating the design and adaptation of heuristic methods to solve hard computational search problems. The main goal is to produce more generally applicable search methodologies. In this chapter we present an overview of previous categorisations of hyper-heuristics and provide a unified classification and definition, which capture the work that is being undertaken in this field. We distinguish between two main hyper-heuristic categories: heuristic selection and heuristic generation. Some representative examples of each category are discussed in detail. Our goals are to clarify the mainfeatures of existing techniques and to suggest new directions for hyper-heuristic research.},
doi = {10.1007/978-1-4419-1665-5_15},
keywords = {search, optimisation, optimization, hyper-heuristics, hyperheuristics, meta-heuristics, metaheuristics, heuristics, classification},
owner = {gxk},
timestamp = {2011.01.28},
url = {http://dx.doi.org/10.1007/978-1-4419-1665-5},
webpdf = {http://www.graham-kendall.com/papers/bhkoow2010.pdf} }