CatCMA with Margin: Stochastic Optimization for Continuous, Integer, and Categorical Variables

• URL: http://arxiv.org/abs/2504.07884v3
• Date: Wed, 16 Apr 2025 04:08:10 GMT
• Title: CatCMA with Margin: Stochastic Optimization for Continuous, Integer, and Categorical Variables
• Authors: Ryoki Hamano, Masahiro Nomura, Shota Saito, Kento Uchida, Shinichi Shirakawa,
• Abstract summary: This study focuses on mixed-variable black-box optimization (MV-BBO)<n>It addresses continuous, integer, and categorical variables.<n>We propose CatCMA with Margin (CatCMAwM), a mixed-category black-box optimization method.
• Score: 9.13074910982872
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This study focuses on mixed-variable black-box optimization (MV-BBO), addressing continuous, integer, and categorical variables. Many real-world MV-BBO problems involve dependencies among these different types of variables, requiring efficient methods to optimize them simultaneously. Recently, stochastic optimization methods leveraging the mechanism of the covariance matrix adaptation evolution strategy have shown promising results in mixed-integer or mixed-category optimization. However, such methods cannot handle the three types of variables simultaneously. In this study, we propose CatCMA with Margin (CatCMAwM), a stochastic optimization method for MV-BBO that jointly optimizes continuous, integer, and categorical variables. CatCMAwM is developed by incorporating a novel integer handling into CatCMA, a mixed-category black-box optimization method employing a joint distribution of multivariate Gaussian and categorical distributions. The proposed integer handling is carefully designed by reviewing existing integer handlings and following the design principles of CatCMA. Even when applied to mixed-integer problems, it stabilizes the marginal probability and improves the convergence performance of continuous variables. Numerical experiments show that CatCMAwM effectively handles the three types of variables, outperforming state-of-the-art Bayesian optimization methods and baselines that simply incorporate existing integer handlings into CatCMA.

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