Hybrid Parameter Search and Dynamic Model Selection for Mixed-Variable Bayesian Optimization

• URL: http://arxiv.org/abs/2206.01409v4
• Date: Fri, 19 Jan 2024 00:23:28 GMT
• Title: Hybrid Parameter Search and Dynamic Model Selection for Mixed-Variable Bayesian Optimization
• Authors: Hengrui Luo, Younghyun Cho, James W. Demmel, Xiaoye S. Li, Yang Liu
• Abstract summary: We propose a new type of hybrid model for Bayesian optimization (BO) adept at managing mixed variables. 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. Our innovations, including dynamic online kernel selection in the surrogate modeling phase, position our hybrid models as an advancement in mixed-variable surrogate models.
• Score: 6.204805504959941
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper 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.

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