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

How are football fixtures worked out?
http://bit.ly/1z0oTAH
Help solve Santa's logistics problems
http://bit.ly/1DXreuW

Latest Blog Post

Snooker: Celebrating 40 years at the Crucible

Random Blog Post

3D Bin Packing, help Santa and share $10,000

Publication(s)

Automating the Packing Heuristic Design Process with Genetic Programming
http://bit.ly/19OfB8C
Evaluating decision-making units under uncertainty using fuzzy multi-objective nonlinear programming
http://bit.ly/2k79ATE
The Entity-to-Algorithm Allocation Problem: Extending the Analysis
http://bit.ly/1yHLiyp
We should be just a number, and we should embrace it
http://bit.ly/2mRCd5m

Graham Kendall: Details of Requested Publication


Citation

Maashi, M; Özcan, E and Kendall, G A Multi-objective Hyper-heuristic based on Choice Function. Expert Systems with Applications, 41 (9): 4475-4493, 2014.

ISSN: 0957-4174


Abstract

Hyper-heuristics are emerging methodologies that perform a search over the space of heuristics in an attempt to solve difficult computational optimization problems. We present a learning selection choice function based hyper-heuristic to solve multi-objective optimization problems. This high level approach controls and combines the strengths of three well-known multi-objective evolutionary algorithms (i.e. NSGAII, SPEA2 and MOGA), utilizing them as the low level heuristics. The performance of the proposed learning hyper-heuristic is investigated on the Walking Fish Group test suite which is a common benchmark for multi-objective optimization. Additionally, the proposed hyper-heuristic is applied to the vehicle crashworthiness design problem as a real-world multi-objective problem. The experimental results demonstrate the effectiveness of the hyper-heuristic approach when compared to the performance of each low level heuristic run on its own, as well as being compared to other approaches including an adaptive multi-method search, namely AMALGAM.


pdf

You can download the pdf of this publication from here


doi

The doi for this publication is 10.1016/j.eswa.2013.12.050 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 (2.240), 2013 (1.965), 2012 (1.854), 2011 (2.203), 2010 (1.926), 2009 (2.908), 2008 (2.596), 2007 (1.177), 2006 (0.957), 2005 (1.236), 2004 (1.247), 2003 (1.067), 2002 (0.786), 2001 (0.321), 2000 (0.405)

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{mok2014, author = {M. Maashi and E. \"{O}zcan and G. Kendall},
title = {A Multi-objective Hyper-heuristic based on Choice Function},
journal = {Expert Systems with Applications},
year = {2014},
volume = {41},
pages = {4475-4493},
number = {9},
note = {ISSN: 0957-4174},
abstract = {Hyper-heuristics are emerging methodologies that perform a search over the space of heuristics in an attempt to solve difficult computational optimization problems. We present a learning selection choice function based hyper-heuristic to solve multi-objective optimization problems. This high level approach controls and combines the strengths of three well-known multi-objective evolutionary algorithms (i.e. NSGAII, SPEA2 and MOGA), utilizing them as the low level heuristics. The performance of the proposed learning hyper-heuristic is investigated on the Walking Fish Group test suite which is a common benchmark for multi-objective optimization. Additionally, the proposed hyper-heuristic is applied to the vehicle crashworthiness design problem as a real-world multi-objective problem. The experimental results demonstrate the effectiveness of the hyper-heuristic approach when compared to the performance of each low level heuristic run on its own, as well as being compared to other approaches including an adaptive multi-method search, namely AMALGAM.},
doi = {10.1016/j.eswa.2013.12.050},
issn = {0957-4174},
owner = {Graham},
timestamp = {2014.01.28},
webpdf = {http://www.graham-kendall.com/papers/mok2014.pdf} }