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
Help solve Santa's logistics problems
http://bit.ly/1DXreuW

Latest Blog Post

How Isaac Newton could help you beat the casino at roulette

Random Blog Post

2015 General Election Prediction: Wisdom of the Crowds

Publication(s)

Alternative hyper-heuristic strategies for multi-method global optimization
http://bit.ly/g1xcMp
Evolving Bin Packing Heuristics with Genetic Programming
http://bit.ly/gPl6d2
Investigating the Use of Local Search for Improving Meta-Hyper-Heuristic Performance
http://bit.ly/1csb3rG
Studying the Effect that a Linear Transformation has on the Time-Series Prediction Ability of an Evolutionary Neural Network
http://bit.ly/eyLaq2

Graham Kendall: Details of Requested Publication


Citation

Han, L and Kendall, G Guided Operators for a Hyper-Heuristic Genetic Algorithm. In Proceedings of AI 2003: Advances in Artificial Intelligence 16th Australian Conference on Artificial Intelligence, pages 807-820, Perth, Australia, Lecture Notes in Computer Science 2903, 2003.


Abstract

We have recently introduced a hyper-heuristic genetic algorithm (hyper-GA) with an adaptive length chromosome which aims to evolve an ordering of low-level heuristics so as to find good quality solutions to given problems. The guided mutation and crossover hyper-GA, the focus of this paper, extends that work. The aim of a guided hyper-GA is to make the dynamic removal and insertion of heuristics more efficient, and evolve sequences of heuristics in order to produce promising solutions more effectively. We apply the algorithm to a geographically distributed training staff and course scheduling problem to compare the computational result with the application of other hyper-GAs. In order to show the robustness of hyper-GAs, we apply our methods to a student project presentation scheduling problem in a UK university and compare results with the application of another hyper-heuristic method.


pdf

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doi

The doi for this publication is 10.1007/978-3-540-24581-0_69 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/b94701

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{hk2003a, author = {L. Han and G. Kendall},
title = {Guided Operators for a Hyper-Heuristic Genetic Algorithm},
booktitle = {Proceedings of AI 2003: Advances in Artificial Intelligence 16th Australian Conference on Artificial Intelligence},
year = {2003},
editor = {T. D. Gedeon and L. Chun Che Fung},
volume = {2903},
series = {Lecture Notes in Computer Science},
pages = {807--820},
address = {Perth, Australia},
month = {3-5 December},
abstract = {We have recently introduced a hyper-heuristic genetic algorithm (hyper-GA) with an adaptive length chromosome which aims to evolve an ordering of low-level heuristics so as to find good quality solutions to given problems. The guided mutation and crossover hyper-GA, the focus of this paper, extends that work. The aim of a guided hyper-GA is to make the dynamic removal and insertion of heuristics more efficient, and evolve sequences of heuristics in order to produce promising solutions more effectively. We apply the algorithm to a geographically distributed training staff and course scheduling problem to compare the computational result with the application of other hyper-GAs. In order to show the robustness of hyper-GAs, we apply our methods to a student project presentation scheduling problem in a UK university and compare results with the application of another hyper-heuristic method.},
doi = {10.1007/978-3-540-24581-0_69},
keywords = {hyper-heuristic, hyperheuristic, scheduling, genetic algorithm},
timestamp = {2007.03.29},
url = {http://dx.doi.org/10.1007/b94701},
webpdf = {http://www.graham-kendall.com/papers/hk2003a.pdf} }