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
I have published a few papers on Sports Scheduling.
http://bit.ly/gVaUqT

Latest Blog Post

How Isaac Newton could help you beat the casino at roulette

Random Blog Post

Using ELO ratings for match result prediction in association football

Publication(s)

Using an Evolutionary Algorithm for the Tuning of a Chess Evaluation Function Based on a Dynamic Boundary Strategy
http://bit.ly/hsgyZ8
Optimising risk reduction: An expected utility approach for marginal risk reduction during regulatory decision making
http://bit.ly/gBihFm
The examination timetabling problem at Universiti Malaysia Pahang: Comparison of a constructive heuristic with an existing software solution
http://bit.ly/fqDS68
Comparison of meta-heuristic algorithms for clustering rectangles
http://bit.ly/eQQ0Kd

Graham Kendall: Details of Requested Publication


Citation

Sabar, N. R; Ayob, M; Kendall, G and Qu, R Automatic Design of Hyper-heuristic Framework with Gene Expression Programming for Combinatorial Optimization problems. IEEE Transactions on Evolutionary Computation, 19 (3): 309-325, 2015.

ISSN: 1089-778X


Abstract

Hyper-heuristic approaches aim to automate heuristic design in order to solve multiple problems instead of designing tailor-made methodologies for individual problems. Hyper-heuristics accomplish this through a high-level heuristic (heuristic selection mechanism and an acceptance criterion). This automates heuristic selection, deciding whether to accept or reject the returned solution. The fact that different problems, or even instances, have different landscape structures and complexity, the design of efficient high-level heuristics can have a dramatic impact on hyper-heuristic performance. In this paper, instead of using human knowledge to design the high-level heuristic, we propose a gene expression programming algorithm to automatically generate, during the instance-solving process, the high-level heuristic of the hyper-heuristic framework. The generated heuristic takes information (such as the quality of the generated solution and the improvement made) from the current problem state as input and decides which low-level heuristic should be selected and the acceptance or rejection of the resultant solution. The benefit of this framework is the ability to generate, for each instance, different highlevel heuristics during the problem-solving process. Furthermore, in order to maintain solution diversity, we utilize a memory mechanism that contains a population of both high-quality and diverse solutions that is updated during the problem-solving process. The generality of the proposed hyper-heuristic is validated against six well-known combinatorial optimization problems, with very different landscapes, provided by the HyFlex software. Empirical results, comparing the proposed hyper-heuristic with state-of-the-art hyper-heuristics, conclude that the proposed hyper-heuristic generalizes well across all domains and achieves competitive, if not superior, results for several instances on all domains.


pdf

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doi

The doi for this publication is 10.1109/TEVC.2014.2319051 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 (3.654), 2013 (5.545), 2012 (4.810), 2011 (3.341), 2010 (4.403), 2009 (4.589), 2008 (3.736), 2007 (2.426), 2006 (3.770), 2005 (3.257), 2004 (3.688), 2003 (2.713), 2002 (1.486), 2001 (1.708)

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{sakq2015a, author = {N. R. Sabar and M. Ayob and G. Kendall and R. Qu},
title = {Automatic Design of Hyper-heuristic Framework with Gene Expression Programming for Combinatorial Optimization problems},
journal = {IEEE Transactions on Evolutionary Computation},
year = {2015},
volume = {19},
pages = {309--325},
number = {3},
note = {ISSN: 1089-778X},
abstract = {Hyper-heuristic approaches aim to automate heuristic design in order to solve multiple problems instead of designing tailor-made methodologies for individual problems. Hyper-heuristics accomplish this through a high-level heuristic (heuristic selection mechanism and an acceptance criterion). This automates heuristic selection, deciding whether to accept or reject the returned solution. The fact that different problems, or even instances, have different landscape structures and complexity, the design of efficient high-level heuristics can have a dramatic impact on hyper-heuristic performance. In this paper, instead of using human knowledge to design the high-level heuristic, we propose a gene expression programming algorithm to automatically generate, during the instance-solving process, the high-level heuristic of the hyper-heuristic framework. The generated heuristic takes information (such as the quality of the generated solution and the improvement made) from the current problem state as input and decides which low-level heuristic should be selected and the acceptance or rejection of the resultant solution. The benefit of this framework is the ability to generate, for each instance, different highlevel heuristics during the problem-solving process. Furthermore, in order to maintain solution diversity, we utilize a memory mechanism that contains a population of both high-quality and diverse solutions that is updated during the problem-solving process. The generality of the proposed hyper-heuristic is validated against six well-known combinatorial optimization problems, with very different landscapes, provided by the HyFlex software. Empirical results, comparing the proposed hyper-heuristic with state-of-the-art hyper-heuristics, conclude that the proposed hyper-heuristic generalizes well across all domains and achieves competitive, if not superior, results for several instances on all domains.},
doi = {10.1109/TEVC.2014.2319051},
issn = {1089-778X},
keywords = {Gene Expression Programming, hyper-heuristics, MAXSAT, Bin Packing, Flow Shop, Personnel Scheduling, Traveling Salesman Problem, Vehicle Routing, CHESC},
owner = {gxk},
timestamp = {2011.06.11},
webpdf = {http://www.graham-kendall.com/papers/sakq2015a.pdf} }