8 citations · 10 across the 6 of their papers we have counts for
6 papers
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…
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…
Theoretical Analysis of Explicit Averaging and Novel Sign Averaging in Comparison-Based Search
Daiki Morinaga, Youhei Akimoto
In black-box optimization, noise in the objective function is inevitable. Noise disrupts the ranking of candidate solutions in comparison-based optimization, possibly deteriorating…
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…
Convergence Rate of the (1+1)-Evolution Strategy with Success-Based Step-Size Adaptation on Convex Quadratic Functions
Daiki Morinaga, Kazuto Fukuchi, Jun Sakuma +1
The (1+1)-evolution strategy (ES) with success-based step-size adaptation is analyzed on a general convex quadratic function and its monotone transformation, that is, $f(x) = g((x…
Global Linear Convergence of Evolution Strategies on More Than Smooth Strongly Convex Functions
Youhei Akimoto, Anne Auger, Tobias Glasmachers +1
Evolution strategies (ESs) are zeroth-order stochastic black-box optimization heuristics invariant to monotonic transformations of the objective function. They evolve a multivariat…