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 to teach Deep Blue to play poker and deliver groceries
http://bit.ly/1DXGeZD
How are football fixtures worked out?
http://bit.ly/1z0oTAH

Latest Blog Post

Snooker: Celebrating 40 years at the Crucible

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Tracking Paper Downloads: Database

Publication(s)

Heuristic Space Diversity Control for Improved Meta-Hyper-Heuristic Performance
http://bit.ly/1C1vIAn
Advanced Population Diversity Measures in Genetic Progaramming
http://bit.ly/i8Yh1C
Evolutionary Strategies vs. Neural Networks; New Evidence from Taiwan on the Divisia Index Debate
http://bit.ly/iiwrjD
Fitness Landscapes and the Andrews-Curtis Conjecture
http://bit.ly/1eUf8Xp

Graham Kendall: Details of Requested Publication


Citation

Burke, E.K; Hyde, M.R and Kendall, G Providing a memory mechanism to enhance the evolutionary design of heuristics. In Proceedings of the 2010 IEEE Congress on Evolutionary Computation (CEC 2010), pages 3883-3890, 2010.


Abstract

Genetic programming approaches have previously been employed in the literature to evolve heuristics for various combinatorial optimisation problems. This paper presents a hyper-heuristic genetic programming methodology to evolve more sophisticated one dimensional bin packing heuristics than have been evolved previously. The heuristics have access to a memory, which allows them to make decisions with some knowledge of their potential future impact. In contrast to previously evolved heuristics for this problem, we show that these heuristics evolve to draw upon this memory in order to facilitate better planning, and improved packings. This fundamental difference enables an evolved heuristic to represent a dynamic packing strategy rather than a fixed packing strategy. A heuristic can change its behaviour depending on the characteristics of the pieces it has seen before, because it has evolved to draw upon its experience.


pdf

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doi

The doi for this publication is 10.1109/CEC.2010.5586388 You can link directly to the original paper, via the doi, from here

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Bibtex

@INPROCEEDINGS{bhk2010, author = {E.K. Burke and M.R. Hyde and G. Kendall},
title = {Providing a memory mechanism to enhance the evolutionary design of heuristics},
booktitle = {Proceedings of the 2010 IEEE Congress on Evolutionary Computation (CEC 2010)},
year = {2010},
pages = {3883--3890},
month = {July 18-23 2010},
organization = {Barcelona, Spain},
abstract = {Genetic programming approaches have previously been employed in the literature to evolve heuristics for various combinatorial optimisation problems. This paper presents a hyper-heuristic genetic programming methodology to evolve more sophisticated one dimensional bin packing heuristics than have been evolved previously. The heuristics have access to a memory, which allows them to make decisions with some knowledge of their potential future impact. In contrast to previously evolved heuristics for this problem, we show that these heuristics evolve to draw upon this memory in order to facilitate better planning, and improved packings. This fundamental difference enables an evolved heuristic to represent a dynamic packing strategy rather than a fixed packing strategy. A heuristic can change its behaviour depending on the characteristics of the pieces it has seen before, because it has evolved to draw upon its experience.},
doi = {10.1109/CEC.2010.5586388},
keywords = {cutting, packing, hyper-heuristics, evolutionary, memory, bin packing, combinatorial mathematics, optimisation},
owner = {gxk},
timestamp = {2010.12.10},
webpdf = {http://www.graham-kendall.com/papers/bhk2010.pdf} }