Abstract
In Bayesian Optimization Algorithm (BOA), to accurately build the best Bayesian network with respect to most metrics is NP-complete. This paper proposes an improved BOA based on incremental model building, which learns Bayesian network structure using PBIL instead of greedy algorithm in BOA. The PBIL is effective to learn better Bayesian network. The simulation results also show that the improved BOA has the better performance than BOA.
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Acknowledgement
This work was partly supported by the Natural Science Foundation of Guangdong Province under Grant No. S2013040015755, the Foundation for Distinguished Young Talents in Higher Education of Guangdong Province under Grant No. 2013LYM_0119, and the Special Foundation for Public Welfare Research and Capacity Building of Guangdong Province under Grant No. 2014A020208087.
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Yao, J., Kong, Y., Yang, L. (2016). Bayesian Optimization Algorithm Based on Incremental Model Building. In: Li, K., Li, J., Liu, Y., Castiglione, A. (eds) Computational Intelligence and Intelligent Systems. ISICA 2015. Communications in Computer and Information Science, vol 575. Springer, Singapore. https://doi.org/10.1007/978-981-10-0356-1_20
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DOI: https://doi.org/10.1007/978-981-10-0356-1_20
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