Variable screening for reduced dependency modelling in Gaussian-based continuous estimation of distribution algorithms

Mishra, Krishna Manjari and Gallagher, Marcus (2012). Variable screening for reduced dependency modelling in Gaussian-based continuous estimation of distribution algorithms. In: 2012 IEEE Congress on Evolutionary Computation (CEC). 2012 IEEE World Congress on Computational Intelligence (IEEE-WCCI 2012), Brisbane Australia, (). 10-15 June 2012. doi:10.1109/CEC.2012.6256482

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Author Mishra, Krishna Manjari
Gallagher, Marcus
Title of paper Variable screening for reduced dependency modelling in Gaussian-based continuous estimation of distribution algorithms
Conference name 2012 IEEE World Congress on Computational Intelligence (IEEE-WCCI 2012)
Conference location Brisbane Australia
Conference dates 10-15 June 2012
Proceedings title 2012 IEEE Congress on Evolutionary Computation (CEC)
Journal name IEEE Congress on Evolutionary Computation. Proceedings
Place of Publication Pitscataway, United States
Publisher IEEE
Publication Year 2012
Sub-type Fully published paper
DOI 10.1109/CEC.2012.6256482
ISBN 9781467315104; 97814673150981
Total pages 8
Language eng
Formatted Abstract/Summary
Estimation of Distribution Algorithms (EDAs) focus on explicitly modelling dependencies between solution variables. A Gaussian distribution over continuous variables is commonly used, with several different covariance matrix structures ranging from diagonal i.e. Univariate Marginal Distribution Algorithm (UMDAc) to full i.e. Estimation of Multivariate Normal density Algorithm (EMNA). A diagonal covariance model is simple but is unable to directly represent covariances between problem variables. On the other hand, a full covariance model requires estimation of (more) parameters from the selected population. In practice, numerical issues can arise with this estimation problem. In addition, the performance of the model has been shown to be sometimes undesirable. In this paper, a modified Gaussian-based continuous EDA is proposed, called sEDA, that provides a mechanism to control the amount of covariance parameters estimated within the Gaussian model. To achieve this, a simple variable screening technique from experimental design is adapted and combined with an idea inspired by the Pareto-front in multi-objective optimization. Compared to EMNAglobal, the algorithm provides improved numerical stability and can use a smaller selected population. Experimental results are presented to evaluate and compare the performance of the algorithm to UMDAc and EMNAglobal.
Keyword Estimation of distribution algorithms
Optimization problems
Screening technique
Q-Index Code E1
Q-Index Status Confirmed Code
Institutional Status UQ
Additional Notes 2012 IEEE World Congress on Computational Intelligence (IEEE-WCCI 2012) consisted of these conferences: the International Joint Conference on Neural Networks (IJCNN 2012), the IEEE International Conference on Fuzzy Systems (FUZZIEEE 2012) and the 2012 IEEE Congress on Evolutionary Computation (IEEE CEC 2012).

 
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