Research on Bayesian Optimization Algorithm Selection Strategy

机译：贝叶斯优化算法选择策略研究

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Probability model accuracy is the base of Bayesian Optimization Algorithm and data sample is the base of construction accuracy model. So sample strategy is critical for the algorithm. In test, tournament selection, truncation selection and proportional selection are adapted to deal with typical dependency-free function, bivariate dependencies function and multivariate dependencies function. The result shows that tournament selection is the best selection strategy for Bayesian Optimization Algorithm, truncation selection and proportional selection are unsuitable for the algorithm.

机译：概率模型精度是贝叶斯优化算法的基础，数据样本是施工精度模型的基础。因此，示例策略对于算法至关重要。在测试中，锦标赛选择，截断选择和比例选择适于处理典型的依赖功能，双相依赖依赖性功能和多变量依赖关系功能。结果表明，锦标赛选择是贝叶斯优化算法的最佳选择策略，截断选择和比例选择不适合该算法。

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《IEEE International Conference on Information and Automation》|2010年||共4页
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Jiang min; Chen Yimin;
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中图分类 TP2-53;
关键词
Bayesian Optimization Algorithm; Tournament selection; Truncation selection; Proportional selection;

机译：贝叶斯优化算法;锦标赛选择;截断选择;比例选择;

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Research on Bayesian Optimization Algorithm Selection Strategy

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