8 papers · 1 filter
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…
Tail Bounds on the Runtime of Categorical Compact Genetic Algorithm
Ryoki Hamano, Kento Uchida, Shinichi Shirakawa +2
The majority of theoretical analyses of evolutionary algorithms in the discrete domain focus on binary optimization algorithms, even though black-box optimization on the categorica…
Natural Gradient Interpretation of Rank-One Update in CMA-ES
Ryoki Hamano, Shinichi Shirakawa, Masahiro Nomura
The covariance matrix adaptation evolution strategy (CMA-ES) is a stochastic search algorithm using a multivariate normal distribution for continuous black-box optimization. In add…