3 papers
math.OC2025
Convergence rate of the (1+1)-evolution strategy on locally strongly convex functions with lipschitz continuous gradient
Daiki Morinaga, Kazuto Fukuchi, Jun Sakuma +1
Evolution strategy (ES) is one of the promising classes of algorithms for black-box continuous optimization. Despite its broad successes in applications, theoretical analysis on th…
cs.NE2025
Challenges of Interaction in Optimizing Mixed Categorical-Continuous Variables
Youhei Akimoto, Xilin Gao, Ze Kai Ng +1
Optimization of mixed categorical-continuous variables is prevalent in real-world applications of black-box optimization. Recently, CatCMA has been proposed as a method for optimiz…
cs.NE2024
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…