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 blog occasionally, feel free to take a look.
http://bit.ly/hq6rMK
The hunt for MH370
http://bit.ly/1DXRLbu

Latest Blog Post

Snooker: Celebrating 40 years at the Crucible

Random Blog Post

Snooker: Celebrating 40 years at the Crucible

Publication(s)

The Entity-to-Algorithm Allocation Problem: Extending the Analysis
http://bit.ly/1yHLiyp
On Nash equilibrium and evolutionarily stable states that are not characterised by the folk theorem
http://bit.ly/1J4KNC0
Competitive travelling salesmen problem: A hyper-heuristic approach
http://bit.ly/KgQhlh
Solving a Practical Examination Timetabling Problem: A Case Study
http://bit.ly/gnJ9XG

Graham Kendall: Details of Requested Publication


Citation

Burke, E.K; Curtois, T; Hyde, M; Kendall, G; Ochoa, G; Petrovic, S; Vázquez-Rodríguez, J.A and Gendreau, M Iterated Local Search vs. Hyper-heuristics: Towards General-Purpose Search Algorithms. In Proceedings of the 2010 IEEE Congress on Evolutionary Computation (CEC 2010), pages 3073-3080, 2010.


Abstract

An important challenge within hyper-heuristic research is to design search methodologies that work well, not only across different instances of the same problem, but also across different problem domains. This article conducts an empirical study involving three different domains in combinatorial optimisation: bin packing, permutation flow shop and personnel scheduling. Using a common software interface (HyFlex), the same algorithms (high-level strategies or hyper-heuristics) can be readily run on all of them. The study is intended as a proof of concept of the proposed interface and domain modules, as a benchmark for testing the generalisation abilities of heuristic search algorithms. Several algorithms and variants from the literature were implemented and tested. From them, the implementation of iterated local search produced the best overall performance. Interestingly, this is one of the most conceptually simple competing algorithms, its advantage as a robust algorithm is probably due to two factors: (i) the simple yet powerful exploration/exploitation balance achieved by systematically combining a perturbation followed by local search; and (ii) its parameter-less nature. We believe that the challenge is still open for the design of robust algorithms that can learn and adapt to the available low-level heuristics, and thus select and apply them accordingly.


pdf

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doi

The doi for this publication is 10.1109/CEC.2010.5586064 You can link directly to the original paper, via the doi, from here

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URL

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Bibtex

@INPROCEEDINGS{bchkopvg2010, author = {E.K. Burke and T. Curtois and M. Hyde and G. Kendall and G. Ochoa and S. Petrovic and J.A. V\'{a}zquez-Rodr\'{i}guez and M. Gendreau},
title = {Iterated Local Search vs. Hyper-heuristics: Towards General-Purpose Search Algorithms},
booktitle = {Proceedings of the 2010 IEEE Congress on Evolutionary Computation (CEC 2010)},
year = {2010},
pages = {3073--3080},
month = {July 18-23 2010},
organization = {Barcelona, Spain},
abstract = {An important challenge within hyper-heuristic research is to design search methodologies that work well, not only across different instances of the same problem, but also across different problem domains. This article conducts an empirical study involving three different domains in combinatorial optimisation: bin packing, permutation flow shop and personnel scheduling. Using a common software interface (HyFlex), the same algorithms (high-level strategies or hyper-heuristics) can be readily run on all of them. The study is intended as a proof of concept of the proposed interface and domain modules, as a benchmark for testing the generalisation abilities of heuristic search algorithms. Several algorithms and variants from the literature were implemented and tested. From them, the implementation of iterated local search produced the best overall performance. Interestingly, this is one of the most conceptually simple competing algorithms, its advantage as a robust algorithm is probably due to two factors: (i) the simple yet powerful exploration/exploitation balance achieved by systematically combining a perturbation followed by local search; and (ii) its parameter-less nature. We believe that the challenge is still open for the design of robust algorithms that can learn and adapt to the available low-level heuristics, and thus select and apply them accordingly.},
doi = {10.1109/CEC.2010.5586064},
keywords = {packing, bin packing, combinatorial mathematics, flow shop scheduling, heuristic programming, optimisation, personnel, search problems, HyFlex software interface, combinatorial optimisation, domain module, exploration-exploitation balance, general-purpose search algorithm, heuristic search algorithm, hyper-heuristic, interface module, iterated local search, low-level heuristics, permutation flow shop, personnel scheduling},
owner = {gxk},
timestamp = {2010.12.10},
webpdf = {http://www.graham-kendall.com/papers/bchkopvg2010.pdf} }