NDLI: Effective structure learning for EDA via L1-regularizedbayesian networks

Content Provider	ACM Digital Library
Author	Xu, Hua Jia, Peifa Cai, Yunpeng Yang, Jiadong
Abstract	The Bayesian optimization algorithm (BOA) uses Bayesian networks to explore the dependencies between decision variables of an optimization problem in pursuit of both faster speed of convergence and better solution quality. In this paper, a novel method that learns the structure of Bayesian networks for BOA is proposed. The proposed method, called L1BOA, uses L1-regularized regression to find the candidate parents of each variable, which leads to a sparse but nearly optimized network structure. The proposed method improves the efficiency of the structure learning in BOA due to the reduction and automated control of network complexity introduced with L1-regularized learning. Experimental studies on different types of benchmark problems are carried out, which show that L1BOA outperforms the standard BOA when no a-priori knowledge about the problem structure is available, and nearly achieves the best performance of BOA that applies explicit complexity controls.
Starting Page	327
Ending Page	334
Page Count	8
File Format	PDF
ISBN	9781450300728
DOI	10.1145/1830483.1830546
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2010-07-07
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	L1-penalized regression Bayesian optimization algorithm Estimation of distribution algorithms Regularization paths Bayesian network
Content Type	Text
Resource Type	Article

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Effective structure learning for EDA via L1-regularizedbayesian networks