50 citations · 60 across the 21 of their papers we have counts for
11 papers · 1 filter
Weight Adaptation for Improving Parallel Performance of Adaptive Stochastic Natural Gradient
Yutaro Yamada, Kento Uchida, Shinichi Shirakawa
Probabilistic model-based evolutionary algorithms are promising for black-box optimization. Specifically, the adaptive stochastic natural gradient (ASNG) adaptively updates its lea…
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…
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…
Warm Starting of CMA-ES for Contextual Optimization Problems
Yuta Sekino, Kento Uchida, Shinichi Shirakawa
Several practical applications of evolutionary computation possess objective functions that receive the design variables and externally given parameters. Such problems are termed c…
Covariance Matrix Adaptation Evolution Strategy for Low Effective Dimensionality
Kento Uchida, Teppei Yamaguchi, Shinichi Shirakawa
Despite the state-of-the-art performance of the covariance matrix adaptation evolution strategy (CMA-ES), high-dimensional black-box optimization problems are challenging tasks. Su…
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…
CMA-ES with Adaptive Reevaluation for Multiplicative Noise
Kento Uchida, Kenta Nishihara, Shinichi Shirakawa
The covariance matrix adaptation evolution strategy (CMA-ES) is a powerful optimization method for continuous black-box optimization problems. Several noise-handling methods have b…
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…
(1+1)-CMA-ES with Margin for Discrete and Mixed-Integer Problems
Yohei Watanabe, Kento Uchida, Ryoki Hamano +3
The covariance matrix adaptation evolution strategy (CMA-ES) is an efficient continuous black-box optimization method. The CMA-ES possesses many attractive features, including inva…