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.

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Publication(s)

Collective Behavior and Kin Selection in Evolutionary IPD
http://bit.ly/if34nF
A Hyper-Heuristic Approach to Strip Packing Problems
http://bit.ly/fkNqJz
Does money matter in inflation forecasting?
http://bit.ly/fRebpX
Multi-drop container loading using a multi-objective evolutionary algorithm
http://bit.ly/1mlJK7A

Graham Kendall: Details of Requested Publication


Citation

Sabar, N. R and Kendall, G Population based Monte Carlo tree search hyper-heuristic for combinatorial optimization problems. Information Sciences, 314: 225-239, 2015.


Abstract

Hyper-heuristics aim to automate the heuristic selection process in order to operate well across different problem instances, or even across different problem domains. A traditional hyper-heuristic framework has two levels, a high level strategy and a set of low level heuristics. The role of the high level strategy is to decide which low level heuristic should be executed at the current decision point. This paper proposes a Monte Carlo tree search hyper-heuristic framework. We model the search space of the low level heuristics as a tree and use Monte Carlo tree search to search through the tree in order to identify the best sequence of low level heuristics to be applied to the current state. To improve the effectiveness of the proposed framework, we couple it with a memory mechanism which contains a population of solutions, utilizing different population updating rules. The generality of the proposed framework is demonstrated using the six domains of the hyper-heuristic competition (CHeSC) test suite (boolean satisfiability (MAX-SAT), one dimensional bin packing, permutation flow shop, personnel scheduling, traveling salesman and vehicle routing with time windows). The results demonstrate that the proposed hyper-heuristic generalizes well over all six domains and obtains competitive, if not better results, when compared to the best known results that have previously been presented in the scientific literature.


pdf

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doi

The doi for this publication is 10.1016/j.ins.2014.10.045 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


Journal Rankings


ISI Web of Knowledge Journal Citation Reports

The Web of Knowledge Journal Citation Reports (often known as ISI Impact Factors) help measure how often an article is cited. You can get an introduction to Journal Citation Reports here. Below I have provided the ISI impact factor for the jourrnal in which this article was published. For complete information I have shown the ISI ranking over a number of years, with the latest ranking highlighted.

2014 (4.038), 2013 (3.893), 2012 (3.643), 2011 (2.833), 2010 (2.836), 2009 (3.291), 2008 (3.095), 2007 (2.147), 2006 (1.003), 2005 (0.723), 2004 (0.540), 2003 (0.447), 2002 (0.361), 2001 (0.264), 2000 (0.322)

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

@ARTICLE{sk2015, author = {N. R. Sabar and G. Kendall},
title = {Population based Monte Carlo tree search hyper-heuristic for combinatorial optimization problems},
journal = {Information Sciences},
year = {2015},
volume = {314},
pages = {225--239},
abstract = {Hyper-heuristics aim to automate the heuristic selection process in order to operate well across different problem instances, or even across different problem domains. A traditional hyper-heuristic framework has two levels, a high level strategy and a set of low level heuristics. The role of the high level strategy is to decide which low level heuristic should be executed at the current decision point. This paper proposes a Monte Carlo tree search hyper-heuristic framework. We model the search space of the low level heuristics as a tree and use Monte Carlo tree search to search through the tree in order to identify the best sequence of low level heuristics to be applied to the current state. To improve the effectiveness of the proposed framework, we couple it with a memory mechanism which contains a population of solutions, utilizing different population updating rules. The generality of the proposed framework is demonstrated using the six domains of the hyper-heuristic competition (CHeSC) test suite (boolean satisfiability (MAX-SAT), one dimensional bin packing, permutation flow shop, personnel scheduling, traveling salesman and vehicle routing with time windows). The results demonstrate that the proposed hyper-heuristic generalizes well over all six domains and obtains competitive, if not better results, when compared to the best known results that have previously been presented in the scientific literature.},
doi = {10.1016/j.ins.2014.10.045},
issn = {0020-0255},
owner = {gxk},
timestamp = {2011.06.11},
webpdf = {http://www.graham-kendall.com/papers/sk2015.pdf} }