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- W4297883938 abstract "Abstract The present paper describes an algorithm for improving the convergence of Bayesian optimization (BO) using the constraints derived from symbolic learning and inference for observations by explanation-based learning (EBL). During the BO process, the constraints based on observations generalized by the domain knowledge (theory) of EBL are applied to maximization of the acquisition function, thereby improving the efficiency of exploration. For the maximization, a (1 + 1)-evolutionary strategy is exploited to incorporate the constraints. In addition, knowledge regarding the improvements is provided as a human-readable representation, which is reflected by the domain theory. Using synthetic and real-world data provided by experiments of lithium-ion batteries to maximize ionic conductivity, the effectiveness of the proposed algorithm is demonstrated. The obtained knowledge is reasonable in the domain theory regarding high ionic conductivity. Thus, the effect of improving explainability for the optimization is shown." @default.
- W4297883938 created "2022-10-01" @default.
- W4297883938 creator A5071342283 @default.
- W4297883938 date "2022-09-12" @default.
- W4297883938 modified "2023-10-18" @default.
- W4297883938 title "Bayesian optimization with constraints based on explanation-based learning and its application in materials research" @default.
- W4297883938 doi "https://doi.org/10.21203/rs.3.rs-2042986/v1" @default.
- W4297883938 hasPublicationYear "2022" @default.
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