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 a member of the Automated Scheduling, Optimisation and Planning Research Group
http://bit.ly/eIQ5XC
I blog occasionally, feel free to take a look.
http://bit.ly/hq6rMK

Latest Blog Post

How Isaac Newton could help you beat the casino at roulette

Random Blog Post

Football Scheduling: News Story

Publication(s)

Towards the 'Decathlon 'Challenge' of search heuristics
http://bit.ly/edfHGs
Problem Difficulty and Code Growth in Genetic Programming
http://bit.ly/eTibpi
Scheduling English football fixtures over holiday periods
http://bit.ly/hkJoTf
Chapter 3: Learning IPD Strategies through Coevolution
http://bit.ly/1eUlKoP

Graham Kendall: Details of Requested Publication


Citation

Allen, S; Burke, E.K; Hyde, M and Kendall, G Evolving Reusable 3D Packing Heuristics with Genetic Programming. In Proceedings of the 11th Annual conference on Genetic and evolutionary computation (GECCO 2009), pages 931-938, 2009.


Abstract

This paper compares the quality of reusable heuristics designed by genetic programming (GP) to those designed by human programmers. The heuristics are designed for the three dimensional knapsack packing problem. Evolutionary computation has been employed many times to search for good quality solutions to such problems. However, actually designing heuristics with GP for this problem domain has never been investigated before. In contrast, the literature shows that it has taken years of experience by human analysts to design the very effective heuristic methods that currently exist. Hyper-heuristics search a space of heuristics, rather than directly searching a solution space. GP operates as a hyper-heuristic in this paper, because it searches the space of heuristics that can be constructed from a given set of components. We show that GP can design simple, yet effective, stand-alone constructive heuristics. While these heuristics do not represent the best in the literature, the fact that they are designed by evolutionary computation, and are human competitive, provides evidence that further improvements in this GP methodology could yield heuristics superior to those designed by humans.


pdf

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doi

The doi for this publication is 10.1145/1569901.1570029 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

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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

@INPROCEEDINGS{abhk2009, author = {S. Allen and E.K. Burke and M. Hyde and G. Kendall},
title = {Evolving Reusable 3D Packing Heuristics with Genetic Programming.},
booktitle = {Proceedings of the 11th Annual conference on Genetic and evolutionary computation (GECCO 2009)},
year = {2009},
pages = {931--938},
month = {Montreal, Canada. 8-12 July},
abstract = {This paper compares the quality of reusable heuristics designed by genetic programming (GP) to those designed by human programmers. The heuristics are designed for the three dimensional knapsack packing problem. Evolutionary computation has been employed many times to search for good quality solutions to such problems. However, actually designing heuristics with GP for this problem domain has never been investigated before. In contrast, the literature shows that it has taken years of experience by human analysts to design the very effective heuristic methods that currently exist. Hyper-heuristics search a space of heuristics, rather than directly searching a solution space. GP operates as a hyper-heuristic in this paper, because it searches the space of heuristics that can be constructed from a given set of components. We show that GP can design simple, yet effective, stand-alone constructive heuristics. While these heuristics do not represent the best in the literature, the fact that they are designed by evolutionary computation, and are human competitive, provides evidence that further improvements in this GP methodology could yield heuristics superior to those designed by humans.},
doi = {10.1145/1569901.1570029},
keywords = {hyper-heuristics, genetic programming, evolution, evolutionary computation, cutting, packing, knapsack, heuristics, desiging heuristics, reusable heuristics},
owner = {est},
timestamp = {2010.03.18},
webpdf = {http://www.graham-kendall.com/papers/abhk2009.pdf} }