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Towards insightful algorithm selection for optimisation using meta-learning concepts

conference contribution
posted on 2008-01-01, 00:00 authored by K Smith-Miles
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.

History

Event

IEEE World Congress on Computational Intelligence (2008 : Hong Kong)

Pagination

4118 - 4124

Publisher

IEEE

Location

Hong Kong

Place of publication

Piscataway, N.J.

Start date

2008-06-01

End date

2008-06-06

ISBN-13

9781424418213

Language

eng

Publication classification

E1 Full written paper - refereed

Copyright notice

2008, IEEE

Editor/Contributor(s)

J Wang

Title of proceedings

WCCI 2008 : IEEE World Congress on Computational Intelligence

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