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

How to teach Deep Blue to play poker and deliver groceries
http://bit.ly/1DXGeZD
I am a member of the Automated Scheduling, Optimisation and Planning Research Group
http://bit.ly/eIQ5XC

Latest Blog Post

Snooker: Celebrating 40 years at the Crucible

Random Blog Post

The Christmas Present Problem: It’s Hard – NP-Hard

Publication(s)

Chapter 4: Genetic Algorithms
http://bit.ly/1sYEs1Q
A Multiobjective Approach for UK Football Scheduling
http://bit.ly/fV4caa
A Particle Swarm Optimisation Approach in the Construction of Optimal Risky Portfolios
http://bit.ly/f6EKmF
Evolving Tiles for Automated Self-Assembly Design
http://bit.ly/dInbHL

Graham Kendall: Details of Requested Publication


Citation

Hyde, M. H; Burke, E. K and Kendall, G Automated code generation by local search. Journal of the Operational Research Society, 64 (12): 1725-1741, 2013.


Abstract

There are many successful evolutionary computation techniques for automatic program generation, with the best known, perhaps, being genetic programming. Genetic programming has obtained human competitive results, even infringing on patented inventions. The majority of the scientific literature on automatic program generation employs such population-based search approaches, to allow a computer system to search a space of programs. In this paper, we present an alternative approach based on local search. There are many local search methodologies that allow successful search of a solution space, based on maintaining a single incumbent solution and searching its neighbourhood. However, use of these methodologies in searching a space of programs has not yet been systematically investigated. The contribution of this paper is to show that a local search of programs can be more successful at automatic program generation than current nature inspired evolutionary computation methodologies.


pdf

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doi

The doi for this publication is 10.1057/jors.2012.149 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 (0.953), 2013 (0.911), 2012 (0.989), 2011 (0.971), 2010 (1.102), 2009 (1.009), 2008 (0.839), 2007 (0.784), 2006 (0.597), 2005 (0.603), 2004 (0.515), 2003 (0.416), 2002 (0.493), 2001 (0.438), 2000 (0.648)

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{hbk2013, author = {M. H. Hyde and E. K. Burke and G. Kendall},
title = {Automated code generation by local search},
journal = {Journal of the Operational Research Society},
year = {2013},
volume = {64},
pages = {1725--1741},
number = {12},
abstract = {There are many successful evolutionary computation techniques for automatic program generation, with the best known, perhaps, being genetic programming. Genetic programming has obtained human competitive results, even infringing on patented inventions. The majority of the scientific literature on automatic program generation employs such population-based search approaches, to allow a computer system to search a space of programs. In this paper, we present an alternative approach based on local search. There are many local search methodologies that allow successful search of a solution space, based on maintaining a single incumbent solution and searching its neighbourhood. However, use of these methodologies in searching a space of programs has not yet been systematically investigated. The contribution of this paper is to show that a local search of programs can be more successful at automatic program generation than current nature inspired evolutionary computation methodologies.},
doi = {10.1057/jors.2012.149},
issn = {0160-5682},
keywords = {hyper-heuristics},
owner = {Graham},
timestamp = {2013.08.03},
webpdf = {http://www.graham-kendall.com/papers/hbk2013.pdf} }