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

Help solve Santa's logistics problems
http://bit.ly/1DXreuW
A Conversation article celebrating Pi
http://bit.ly/1DXuXbV

Latest Blog Post

Snooker: Celebrating 40 years at the Crucible

Random Blog Post

What is Operations Research?

Publication(s)

A Multi-objective Hyper-heuristic based on Choice Function
http://bit.ly/1f8GQgU
Evolutionary Computation and Games (Invited Review)
http://bit.ly/f6qvUI
Guided Operators for a Hyper-Heuristic Genetic Algorithm
http://bit.ly/hd4Erh
EnHiC: An enforced hill climbing based system for general game playing
http://bit.ly/1VOTCY2

Graham Kendall: Details of Requested Publication


Citation

Gibbs, J; Kendall, G and Özcan, E Scheduling English Football Fixtures over the Holiday Period Using Hyper-heuristics. In Proceedings of Problem Parallel Solving from Nature (PPSN XI), Sep 2010, pages 496-505, Springer Berlin / Heidelberg, Lecture Notes in Computer Science 6238, 2011.


Abstract

One of the annual issues that has to be addressed in English football is producing a fixture schedule for the holiday periods that reduces the travel distance for the fans and players. This problem can be seen as a minimisation problem which must abide to the constraints set by the Football Association. In this study, the performance of selection hyper-heuristics is investigated as a solution methodology. Hyper-heuristics aim to automate the process of selecting and combining simpler heuristics to solve computational search problems. A selection hyper-heuristic stores a single candidate solution in memory and iteratively applies selected low level heuristics to improve it. The results show that the learning hyper-heuristics outperform some previously proposed approaches and solutions published by the Football Association.


pdf

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doi

The doi for this publication is 10.1007/978-3-642-15844-5_50 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://www.springer.com/computer/bioinformatics/book/978-3-642-15843-8

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{gko2011, author = {J. Gibbs and G. Kendall and E. \"{O}zcan},
title = {Scheduling English Football Fixtures over the Holiday Period Using Hyper-heuristics},
booktitle = {Proceedings of Problem Parallel Solving from Nature (PPSN XI), Sep 2010},
year = {2011},
editor = {R. Schaefer and C. Cotta and J Kolodziej and G.Rudolph},
volume = {6238},
series = {Lecture Notes in Computer Science},
pages = {496--505},
month = {11-15 September 2010},
publisher = {Springer Berlin / Heidelberg},
abstract = {One of the annual issues that has to be addressed in English football is producing a fixture schedule for the holiday periods that reduces the travel distance for the fans and players. This problem can be seen as a minimisation problem which must abide to the constraints set by the Football Association. In this study, the performance of selection hyper-heuristics is investigated as a solution methodology. Hyper-heuristics aim to automate the process of selecting and combining simpler heuristics to solve computational search problems. A selection hyper-heuristic stores a single candidate solution in memory and iteratively applies selected low level heuristics to improve it. The results show that the learning hyper-heuristics outperform some previously proposed approaches and solutions published by the Football Association.},
doi = {10.1007/978-3-642-15844-5_50},
keywords = {hyper-heuristics, sport, soccer, football, scheduling},
owner = {gxk},
timestamp = {2010.12.09},
url = {http://www.springer.com/computer/bioinformatics/book/978-3-642-15843-8},
webpdf = {http://www.graham-kendall.com/papers/gko2011.pdf} }