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 have wriiten a number of articles for TheConversation
http://bit.ly/1yWlOkE
I am involved with a spin out company that specialises in Strategic Resource Planning
http://bit.ly/eTPZO2

Latest Blog Post

Snooker: Celebrating 40 years at the Crucible

Random Blog Post

My First Java Project

Publication(s)

A New Approach to Packing Non-Convex Polygons Using the No Fit Polygon and Meta-Heuristic and Evolutionary Algorithms
http://bit.ly/eMYCKs
Chapter 4: Genetic Algorithms
http://bit.ly/1h1JBCi
A learning-guided multi-objective evolutionary algorithm for constrained portfolio optimization
http://bit.ly/1wqQplE
Tabu Exponential Monte-Carlo with Counter Heuristic for Examination Timetabling.
http://bit.ly/fjry8k

Graham Kendall: Details of Requested Publication


Citation

Grobler, J; Engelbrecht, A. P; Kendall, G. and Yadavalli, S Investigating the Use of Local Search for Improving Meta-Hyper-Heuristic Performance. In Proceedings of the 2012 IEEE Congress on Evolutionary Computation, pages 3277-3284, 2012.


Abstract

This paper investigates the use of local search strategies to improve the performance of a meta-hyper-heuristic algorithm, a hyper-heuristic which employs one or more metaheuristics as low-level heuristics. Alternative mechanisms for selecting the solutions to be refined further by means of local search, as well as the intensity of subsequent refinement in terms of number of allowable function evaluations, are investigated. Furthermore, defining a local search as one of the low-level heuristics versus applying the algorithm directly to the solution space is also investigated. Performance is evaluated on a diverse set of floating-point benchmark problems. The addition of local search was found to improve algorithm results significantly. Random selection of solutions for further refinement was identified as the best selection strategy and a higher intensity of refinement was identified as most desirable. Better results were obtained by applying the local search algorithm directly to the search space instead of defining it as a low-level heuristic.


pdf

You can download the pdf of this publication from here


doi

The doi for this publication is 10.1109/CEC.2012.6252970 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



URL

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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

@INPROCEEDINGS{geky2012, author = {J. Grobler and A. P. Engelbrecht and Graham Kendall and S. Yadavalli},
title = {Investigating the Use of Local Search for Improving Meta-Hyper-Heuristic Performance},
booktitle = {Proceedings of the 2012 IEEE Congress on Evolutionary Computation},
year = {2012},
pages = {3277--3284},
organization = {June, 10-15, 2012 - Brisbane, Australia},
abstract = {This paper investigates the use of local search strategies to improve the performance of a meta-hyper-heuristic algorithm, a hyper-heuristic which employs one or more metaheuristics as low-level heuristics. Alternative mechanisms for selecting the solutions to be refined further by means of local search, as well as the intensity of subsequent refinement in terms of number of allowable function evaluations, are investigated. Furthermore, defining a local search as one of the low-level heuristics versus applying the algorithm directly to the solution space is also investigated. Performance is evaluated on a diverse set of floating-point benchmark problems. The addition of local search was found to improve algorithm results significantly. Random selection of solutions for further refinement was identified as the best selection strategy and a higher intensity of refinement was identified as most desirable. Better results were obtained by applying the local search algorithm directly to the search space instead of defining it as a low-level heuristic.},
doi = {10.1109/CEC.2012.6252970},
keywords = {hyper-heuristics, hyperheuristcs,},
owner = {gxk},
timestamp = {2007.03.29},
webpdf = {http://www.graham-kendall.com/papers/geky2012.pdf} }