6 papers
On the Generalization Bounds of Symbolic Regression with Genetic Programming
Masahiro Nomura, Ryoki Hamano, Isao Ono
Symbolic regression (SR) with genetic programming (GP) aims to discover interpretable mathematical expressions directly from data. Despite its strong empirical success, the theoret…
Convergence Analysis of Evolution Strategies for Mixed-Integer Optimization
Ryoki Hamano, Kento Uchida, Shinichi Shirakawa
Mixed-integer extensions of evolution strategies (ES) that discretize selected coordinates of sampled continuous vectors often impose a lower bound on the standard deviation of int…
Adaptive Stochastic Natural Gradient Method for Safe Optimization on Binary Space
Kento Uchida, Ryoki Hamano, Masahiro Nomura +1
Optimization problems in real-world applications across the medical and engineering domains often involve potential risks when evaluating candidate solutions. Safe optimization aim…
Diversified Residual Symbolic Regression
Koki Ikeda, Masahiro Nomura, Ryoki Hamano
Symbolic regression (SR) aims to discover explicit mathematical expressions that explain observed data and is widely used in domains where interpretability is essential. Because in…
CatCMA with Margin for Single- and Multi-Objective Mixed-Variable Black-Box Optimization
Ryoki Hamano, Masahiro Nomura, Shota Saito +2
This study focuses on mixed-variable black-box optimization (MV-BBO), addressing continuous, integer, and categorical variables. Many real-world MV-BBO problems involve dependencie…
CatCMA : Stochastic Optimization for Mixed-Category Problems
Ryoki Hamano, Shota Saito, Masahiro Nomura +2
Black-box optimization problems often require simultaneously optimizing different types of variables, such as continuous, integer, and categorical variables. Unlike integer variabl…