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

Can ants play chess? Yes they can!
http://bit.ly/1yW3UhX
I have published a number of papers on Cutting and Packing
http://bit.ly/dQPw7T

Latest Blog Post

Snooker: Celebrating 40 years at the Crucible

Random Blog Post

Update: Displaying bibtex on web site

Publication(s)

Chapter 1: Introduction
http://bit.ly/1tzAi1K
Scheduling English Football Fixtures: Consideration of Two Conflicting Objectives
http://bit.ly/er0RSP
Hyper-heuristics: a survey of the state of the art
http://bit.ly/1eSDAeb
An Evolutionary Methodology for the Automated Design of Cellular Automaton-based Complex Systems
http://bit.ly/hlJNZh

Graham Kendall: Details of Requested Publication


Citation

Burke, E. K; Curtois, T; Kendall, G; Hyde, M; Ochoa, G; Vazquez-Rodriguez, J.A and Petrovic, S Towards the 'Decathlon 'Challenge' of search heuristics. In Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference (GECCO 2009): Late Breaking Papers, pages 2205-2208, Montreal, Canada. 8-12 July, 2009.

Please note that this paper appeared in the Late Breaking Papers proceedings


Abstract

We present an object oriented framework for designing and evaluating heuristic search algorithms that achieve a high level of generality and work well on a wide range of combinatorial optimization problems. Our framework, named HyFlex, differs from most software tools for meta-heuristics and evolutionary computation in that it provides the algorithm components that are problem-specific instead of those which are problem-independent. In this way, we simultaneously liberate algorithm designers from needing to know the details of the problem domains; and prevent them from incorporating additional problem specific information in their algorithms. The efforts need instead to be focused on designing high-level strategies to intelligently combine the provided problem specific algorithmic components. We plan to propose a challenge, based on our framework, where the winners will be those algorithms with a better overall performance across all of the different domains. Using an Olympic metaphor, we are not solely focussed on the 100 meters race, but instead on the decathlon of modern search methodologies.


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doi

The doi for this publication is 10.1145/1570256.1570303 You can link directly to the original paper, via the doi, from here

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Bibtex

@INPROCEEDINGS{bckhov2009, author = {E. K. Burke and T. Curtois and G. Kendall and M. Hyde and G. Ochoa and J.A. Vazquez-Rodriguez and S. Petrovic},
title = {Towards the 'Decathlon 'Challenge' of search heuristics},
booktitle = {Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference (GECCO 2009): Late Breaking Papers},
year = {2009},
pages = {2205--2208},
address = {Montreal, Canada. 8-12 July},
note = {Please note that this paper appeared in the Late Breaking Papers proceedings},
abstract = {We present an object oriented framework for designing and evaluating heuristic search algorithms that achieve a high level of generality and work well on a wide range of combinatorial optimization problems. Our framework, named HyFlex, differs from most software tools for meta-heuristics and evolutionary computation in that it provides the algorithm components that are problem-specific instead of those which are problem-independent. In this way, we simultaneously liberate algorithm designers from needing to know the details of the problem domains; and prevent them from incorporating additional problem specific information in their algorithms. The efforts need instead to be focused on designing high-level strategies to intelligently combine the provided problem specific algorithmic components. We plan to propose a challenge, based on our framework, where the winners will be those algorithms with a better overall performance across all of the different domains. Using an Olympic metaphor, we are not solely focussed on the 100 meters race, but instead on the decathlon of modern search methodologies.},
doi = {10.1145/1570256.1570303},
keywords = {hyperheuristics, hyper-heuristic, competition, meta-heuristics, metaheuristics, heuristics, flow shop problem, bin packing, personnel scheduling, rostering, boolean satisfiability},
owner = {gxk},
timestamp = {2011.01.16} }