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This paper presents a new type of hybrid model for Bayesian optimization (BO)adept at managing mixed variables, encompassing both quantitative (continuousand integer) and qualitative (categorical) types. Our proposed new hybridmodels (named hybridM) merge the Monte Carlo Tree Search structure (MCTS) forcategorical variables with Gaussian Processes (GP) for continuous ones. hybridMleverages the upper confidence bound tree search (UCTS) for MCTS strategy,showcasing the tree architecture's integration into Bayesian optimization. Ourinnovations, including dynamic online kernel selection in the surrogatemodeling phase and a unique UCTS search strategy, position our hybrid models asan advancement in mixed-variable surrogate models. Numerical experimentsunderscore the superiority of hybrid models, highlighting their potential inBayesian optimization.

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