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2016 | OriginalPaper | Chapter

An Improved DBOA Based on Estimation of Model Similarity

Authors : Yuyan Kong, Jintao Yao, Lei Yang

Published in: Computational Intelligence and Intelligent Systems

Publisher: Springer Singapore

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Abstract

In DBOA, to build accurately the best Bayesian network with respect to most metrics is NP-complete and the high time complexity of learning the model structure becomes a bottleneck of DBOA for real application. Consequently, in order to decrease the asymptotic time complexity of model building and make the algorithm more practical even for extremely large and complex problem, this paper presents adaptive sporadic model building based on estimation of model similarity as an efficiency enhancement technique of DBOA. The results show that performing the adaptive model building in DBOA can reduce the number of building model under no increasing on the number of generation and population size necessary to converge to optimal solutions, and achieve a better trade-off between the convergence speed and convergence results.

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Metadata
Title
An Improved DBOA Based on Estimation of Model Similarity
Authors
Yuyan Kong
Jintao Yao
Lei Yang
Copyright Year
2016
Publisher
Springer Singapore
DOI
https://doi.org/10.1007/978-981-10-0356-1_21

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