When does dependency modelling help? Using a randomized landscape generator to compare algorithms in terms of problem structure

Morgan, Rachael and Gallagher, Marcus (2010). When does dependency modelling help? Using a randomized landscape generator to compare algorithms in terms of problem structure. In: Robert Schaefer, Carlos Cotta, Joanna Kołodziej and Günter Rudolph, Proceedings of: Parallel Problem Solving from Nature – PPSN XI. 11th International Conference. Part 1. Parallel Problem Solving from Nature, Kraków, Poland, (94-103). 11-15 September 2010. doi:10.1007/978-3-642-15844-5_10

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Author Morgan, Rachael
Gallagher, Marcus
Title of paper When does dependency modelling help? Using a randomized landscape generator to compare algorithms in terms of problem structure
Conference name Parallel Problem Solving from Nature
Conference location Kraków, Poland
Conference dates 11-15 September 2010
Proceedings title Proceedings of: Parallel Problem Solving from Nature – PPSN XI. 11th International Conference. Part 1   Check publisher's open access policy
Journal name Lecture Notes in Computer Science   Check publisher's open access policy
Place of Publication Heidelberg, Germany
Publisher Springer
Publication Year 2010
Sub-type Fully published paper
DOI 10.1007/978-3-642-15844-5_10
Open Access Status
ISBN 9783642158438
3642158439
ISSN 0302-9743
Editor Robert Schaefer
Carlos Cotta
Joanna Kołodziej
Günter Rudolph
Volume 6238
Start page 94
End page 103
Total pages 10
Collection year 2011
Language eng
Formatted Abstract/Summary
In this paper we extend a previously proposed randomized landscape generator in combination with a comparative experimental methodology to study the behaviour of continuous metaheuristic optimization algorithms. In particular, we generate landscapes with parameterised, linear ridge structure and perform pairwise comparisons of algorithms to gain insight into what kind of problems are easy and difficult for one algorithm instance relative to another. We apply this methodology to investigate the specific issue of explicit dependency modelling in simple continuous Estimation of Distribution Algorithms. Experimental results reveal specific examples of landscapes (with certain identifiable features) where dependency modelling is useful, harmful or has little impact on average algorithm performance. The results are related to some previous intuition about the behaviour of these algorithms, but at the same time lead to new insights into the relationship between dependency modelling in EDAs and the structure of the problem landscape. The overall methodology is quite general and could be used to examine specific features of other algorithms.
© Springer-Verlag Berlin Heidelberg 2010
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ
Additional Notes Presented during Session 9 as paper #165.

 
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Created: Thu, 10 Mar 2011, 10:22:29 EST by Dr Marcus Gallagher on behalf of School of Information Technol and Elec Engineering