Graham Kendall
Various Images

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 the chair of the MISTA (Multidisciplinary International Conference on Scheduling: Theory and Applications)
http://bit.ly/hvZIaN
How are university examinations scheduled?
http://bit.ly/1z0pG4s

Latest Blog Post

Snooker: Celebrating 40 years at the Crucible

Random Blog Post

Time to switch to Java for a football prediction project

Publication(s)

Population based Local Search for university course timetabling problems
http://bit.ly/1mlJLZj
A Survey of NP-Complete Puzzles
http://bit.ly/hsErcv
A decision support approach for group decision making under risk and uncertainty
http://bit.ly/i4aRjQ
An adaptive multi-population artificial bee colony algorithm for dynamic optimisation problems
http://bit.ly/29btjbV

Graham Kendall: Details of Requested Publication


Citation

Han, L and Kendall, G An investigation of a tabu assisted hyper-heuristic genetic algorithm. In Proceedings of the The IEEE 2003 Congress on Evolutionary Computation (CEC2003), pages 2230-2237, Canberra, Australia, 2003.


Abstract

This paper investigates a tabu assisted genetic algorithm based hyper-heuristic (hyper-TGA) for personnel scheduling problems. We recently introduced a hyper-heuristic genetic algorithm (hyper-GA) with an adaptive length chromosome which aims to evolve an ordering of low-level heuristics in order to find good quality solutions to given problems. The addition of a tabu method, the focus of this paper, extends that work. The aim of adding a tabu list to the hyper-GA is to indicate the efficiency of each gene within the chromosome. We apply the algorithm to a geographically distributed training staff and course scheduling problem and compare the computational results with our previous hyper-GA.


pdf

You can download the pdf of this publication from here


doi

The doi for this publication is 10.1109/CEC.2003.1299949 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

This pubication does not have a URL associated with it.

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{hk2003, author = {L. Han and G. Kendall},
title = {An investigation of a tabu assisted hyper-heuristic genetic algorithm},
booktitle = {Proceedings of the The IEEE 2003 Congress on Evolutionary Computation (CEC2003)},
year = {2003},
volume = {3},
pages = {2230--2237},
address = {Canberra, Australia},
month = {Dec 8 - 12},
abstract = {This paper investigates a tabu assisted genetic algorithm based hyper-heuristic (hyper-TGA) for personnel scheduling problems. We recently introduced a hyper-heuristic genetic algorithm (hyper-GA) with an adaptive length chromosome which aims to evolve an ordering of low-level heuristics in order to find good quality solutions to given problems. The addition of a tabu method, the focus of this paper, extends that work. The aim of adding a tabu list to the hyper-GA is to indicate the efficiency of each gene within the chromosome. We apply the algorithm to a geographically distributed training staff and course scheduling problem and compare the computational results with our previous hyper-GA.},
comment = {IEEE Catalog Number: 03TH8674, ISBN: 0-7803-7804-0},
doi = {10.1109/CEC.2003.1299949},
keywords = {hyper-heuristic, hyperheyristic, genetic algorithm, tabu search, personnel scheduling, rostering, timetabling},
timestamp = {2007.03.29},
webpdf = {http://www.graham-kendall.com/papers/hk2003.pdf} }