Introducing assignment functions to Bayesian optimization algorithms

Authors:
Masaharu Munetomo;Naoya Murao;Kiyoshi Akama
Affiliations:
Division of Large-scale Computing Systems, Information Initiative Center, Hokkaido University, Sapporo, Hokkaido 060-0811, Japan;Division of Systems and Information Engineering, Graduate School of Engineering, Hokkaido University, Sapporo, Hokkaido 060-0811, Japan;Division of Large-scale Computing Systems, Information Initiative Center, Hokkaido University, Sapporo, Hokkaido 060-0811, Japan
Venue:
Information Sciences: an International Journal
Year:
2008

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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.