Mixed-Variable Black-Box Optimisation Using Value Proposal Trees

Many real-world optimisation problems are defined over both categorical and continuous variables, yet efficient optimisation methods such as Bayesian Optimisation (BO) are ill-equipped to handle such mixed-variable search spaces. The optimisation breadth introduced by categorical variables in the mixed-input setting has seen recent approaches operating on local trust regions, but these methods can be greedy in suboptimal regions of the search space. In this paper, we adopt a holistic view and aim to consolidate optimisation of the categorical and continuous sub-spaces under a single acquisition metric. We develop a tree-based method which retains a global view of the optimisation spaces by identifying regions in the search space with high potential candidates which we call value proposals. Our method uses these proposals to make selections on both the categorical and continuous components of the input. We show that this approach significantly outperforms existing mixed-variable optimisation approaches across several mixed-variable black-box optimisation tasks.

Authors

• Yan Zuo Amazon
• Vu Nguyen Amazon
• Amir Dezfouli BIMLOGIQ
• David Alexander CSIRO
• Benjamin Ward Muir CSIRO
• Iadine Chades CSIRO

Keywords

• ML: Auto ML and Hyperparameter Tuning
• ML: Probabilistic Methods