50 citations · 60 across the 21 of their papers we have counts for
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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…
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…
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…