Towards insightful algorithm selection for optimisation using meta-learning concepts

Smith-Miles, Kate 2008, Towards insightful algorithm selection for optimisation using meta-learning concepts, in WCCI 2008 : IEEE World Congress on Computational Intelligence, IEEE, Piscataway, N.J., pp. 4118-4124.

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Title Towards insightful algorithm selection for optimisation using meta-learning concepts
Author(s) Smith-Miles, Kate
Conference name IEEE World Congress on Computational Intelligence (2008 : Hong Kong)
Conference location Hong Kong
Conference dates 1-6 June 2008
Title of proceedings WCCI 2008 : IEEE World Congress on Computational Intelligence
Editor(s) Wang, Jun
Publication date 2008
Conference series IEEE World Congress on Computational Intelligence
Start page 4118
End page 4124
Publisher IEEE
Place of publication Piscataway, N.J.
Summary In this paper we propose a meta-learning inspired framework for analysing the performance of meta-heuristics for optimization problems, and developing insights into the relationships between search space characteristics of the problem instances and algorithm performance. Preliminary results based on several meta-heuristics for well-known instances of the Quadratic Assignment Problem are presented to illustrate the approach using both supervised and unsupervised learning methods.
ISBN 9781424418213
Language eng
Field of Research 080109 Pattern Recognition and Data Mining
HERDC Research category E1 Full written paper - refereed
Copyright notice ©2008, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30018286

Document type: Conference Paper
Collection: School of Engineering and Information Technology
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