19 citations · 38 across the 8 of their papers we have counts for
8 papers
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…
Harnessing the Latent Diffusion Model for Training-Free Image Style Transfer
Kento Masui, Mayu Otani, Masahiro Nomura +1
Diffusion models have recently shown the ability to generate high-quality images. However, controlling its generation process still poses challenges. The image style transfer task…
CMA-ES for Discrete and Mixed-Variable Optimization on Sets of Points
Kento Uchida, Ryoki Hamano, Masahiro Nomura +2
Discrete and mixed-variable optimization problems have appeared in several real-world applications. Most of the research on mixed-variable optimization considers a mixture of integ…
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…
CMA-ES for Safe Optimization
Kento Uchida, Ryoki Hamano, Masahiro Nomura +2
In several real-world applications in medical and control engineering, there are unsafe solutions whose evaluations involve inherent risk. This optimization setting is known as saf…
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…
cmaes: A Simple yet Practical Python Library for CMA-ES
Masahiro Nomura, Masashi Shibata, Ryoki Hamano
The covariance matrix adaptation evolution strategy (CMA-ES) has been highly effective in black-box continuous optimization, as demonstrated by its success in both benchmark proble…
CMA-ES with Learning Rate Adaptation
Masahiro Nomura, Youhei Akimoto, Isao Ono
The covariance matrix adaptation evolution strategy (CMA-ES) is one of the most successful methods for solving continuous black-box optimization problems. A practically useful aspe…