Introducing assignment functions to Bayesian optimization algorithms

Munetomo, Masaharu; Murao, Naoya; Akama, Kiyoshi

Information Sciences, 2008, 178(1), 152-163

Permalink : https://hdl.handle.net/2115/30305

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アクセス権	open access
URI	https://hdl.handle.net/2115/30305
タイトル	Introducing assignment functions to Bayesian optimization algorithms
著者	Munetomo, Masaharu ; Murao, Naoya ; Akama, Kiyoshi
e-Rad_Researcher 00281783 著者名 Munetomo, Masaharu
著者名 Murao, Naoya
著者名 Akama, Kiyoshi
言語	英語
キーワード	Evolutionary computation
	Bayesian optimization algorithms
	Assignment functions
発行日	2008-01-02
出版者	Elsevier Inc.
収録物名	Information Sciences
巻(号)	178(1)
ページ	152-163
抄録	In this paper, we improve Bayesian optimization algorithms by introducing proportionate and rank-based assignment functions. A Bayesian optimization algorithm builds a Bayesian network from a selected sub-population of promising solutions, and this probabilistic model is employed to generate the offspring of the next generation. Our method assigns each solution a relative significance based on its fitness, and this information is used in building the Bayesian network model. These assignment functions can improve the quality of the model without performing an explicit selection on the population. Numerical experiments demonstrate the effectiveness of this method compared to a conventional BOA.
資源タイプ	学術雑誌論文
出版タイプ	Accepted Manuscript
PISSN	0020-0255
関連情報	URI http://www.sciencedirect.com/science/journal/00200255
関連情報 (isVersionOf)	出版社版DOI https://doi.org/10.1016/j.ins.2007.08.014