TAM-EDA: multivariate t distribution, archive and mutation based estimation of distribution algorithm

Gao, Bo and Wood, Ian (2012) TAM-EDA: multivariate t distribution, archive and mutation based estimation of distribution algorithm. ANZIAM Journal, 54 SUPPL: C720-C746. doi:10.0000/anziamj.v54i0.6365

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Author Gao, Bo
Wood, Ian
Title TAM-EDA: multivariate t distribution, archive and mutation based estimation of distribution algorithm
Journal name ANZIAM Journal   Check publisher's open access policy
ISSN 1446-1811
1446-8735
Publication date 2012
Year available 2014
Sub-type Article (original research)
DOI 10.0000/anziamj.v54i0.6365
Open Access Status DOI
Volume 54
Issue SUPPL
Start page C720
End page C746
Total pages 27
Place of publication Cambridge, United Kingdom
Publisher Cambridge University Press
Collection year 2015
Language eng
Formatted abstract
We present a novel estimation of a distribution algorithm (eda), tam-eda, which uses a multivariate t distribution model, an archive population and a mutation operation to escape local minima, avoid premature convergence and utilize a record of the best solutions. Earlier edas used multivariate normal distributions to model low-cost regions of the search space. The multivariate t distribution has heavier tails and so is more likely to maintain diversity, while still allowing convergence to occur. The current population of potential solutions has limited ability to represent all the best regions of the search space explored so far. The archive allows storage of a larger population of promising solutions, which are then used in model building. However, the eda model and archive may still become stuck at suboptimal solutions, so to combat this we introduce a decomposition mutation operation which retains most of the attributes of a current solution but attempts large changes in others. A comparison with generic eda, genetic algorithms and the Nelder–Mead method shows that tam-eda is an effective optimization algorithm for a range of test problems.
Keyword Optimization
Estimation of distribution algorithms
Decomposition Mutation
t Distribution
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ
Additional Notes Published January 14, 2014, as part of the Proceedings of the 16th Biennial Computational Techniques and Applications Conference.

Document type: Journal Article
Sub-type: Article (original research)
Collections: School of Mathematics and Physics
Official 2015 Collection
 
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Created: Thu, 30 Jan 2014, 18:06:54 EST by Mr Ian Wood on behalf of School of Mathematics & Physics