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Introducing assignment functions to Bayesian optimization algorithms

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Title: Introducing assignment functions to Bayesian optimization algorithms
Authors: Munetomo, Masaharu Browse this author →KAKEN DB
Murao, Naoya Browse this author
Akama, Kiyoshi Browse this author
Keywords: Evolutionary computation
Bayesian optimization algorithms
Assignment functions
Issue Date: 2-Jan-2008
Publisher: Elsevier Inc.
Journal Title: Information Sciences
Volume: 178
Issue: 1
Start Page: 152
End Page: 163
Publisher DOI: 10.1016/j.ins.2007.08.014
Abstract: 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.
Type: article (author version)
Appears in Collections:情報基盤センター (Information Initiative Center) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)

Submitter: 棟朝 雅晴

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