Hybrid Parameter Search and Dynamic Model Selection for Mixed-Variable Bayesian Optimization
提出一种混合模型hybridM，结合蒙特卡洛树搜索处理分类变量和高斯过程处理连续变量，并引入动态在线核选择，在混合变量贝叶斯优化中表现更优。
This article presents a new type of hybrid model for Bayesian optimization (BO) adept at managing mixed variables, encompassing both quantitative (continuous and integer) and qualitative (categorical) types. Our proposed new hybrid models (named hybridM) merge the Monte Carlo Tree Search structure (MCTS) for categorical variables with Gaussian Processes (GP) for continuous ones. hybridM leverages the upper confidence bound tree search (UCTS) for MCTS strategy, showcasing the tree architecture’s integration into Bayesian optimization. Our innovations, including dynamic online kernel selection in the surrogate modeling phase and a unique UCTS search strategy, position our hybrid models as an advancement in mixed-variable surrogate models. Numerical experiments underscore the superiority of hybrid models, highlighting their potential in Bayesian optimization. Supplementary materials for this article are available online.