71 citations · 368 across the 49 of their papers we have counts for
19 papers · 1 filter
Mixed-Categorical Black-Box Optimization via Information-Geometric Bilevel Decomposition
Marc Ong, Shinichi Shirakawa, Youhei Akimoto
Mixed categorical-continuous optimization arises in many practical domains, yet remains challenging. In the black-box setting, evolution strategy-based approaches have shown promis…
Accelerating Black-Box Bilevel Optimization with Rank-Based Upper-Level Value Function Approximation
Marc Ong, Youhei Akimoto
Bilevel optimization is a field of significant theoretical and practical interest, yet solving such optimization problems remains challenging. Evolutionary methods have been employ…
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…
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…
CMA-ES with Learning Rate Adaptation: Can CMA-ES with Default Population Size Solve Multimodal and Noisy Problems?
Masahiro Nomura, Youhei Akimoto, Isao Ono
The covariance matrix adaptation evolution strategy (CMA-ES) is one of the most successful methods for solving black-box continuous optimization problems. One practically useful as…
Covariance Matrix Adaptation Evolutionary Strategy with Worst-Case Ranking Approximation for Min--Max Optimization and its Application to Berthing Control Tasks
Atsuhiro Miyagi, Yoshiki Miyauchi, Atsuo Maki +3
In this study, we consider a continuous min--max optimization problem whose objective function is a black-box. We propose a…
Adaptive Scenario Subset Selection for Worst-Case Optimization and its Application to Well Placement Optimization
Atsuhiro Miyagi, Kazuto Fukuchi, Jun Sakuma +1
In this study, we consider simulation-based worst-case optimization problems with continuous design variables and a finite scenario set. To reduce the number of simulations require…
Black-Box Min--Max Continuous Optimization Using CMA-ES with Worst-case Ranking Approximation
Atsuhiro Miyagi, Kazuto Fukuchi, Jun Sakuma +1
In this study, we investigate the problem of min-max continuous optimization in a black-box setting . A popular approach updates and simultaneously…
Monotone Improvement of Information-Geometric Optimization Algorithms with a Surrogate Function
Youhei Akimoto
A surrogate function is often employed to reduce the number of objective function evaluations for optimization. However, the effect of using a surrogate model in evolutionary appro…
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…
Diagonal Acceleration for Covariance Matrix Adaptation Evolution Strategies
Youhei Akimoto, Nikolaus Hansen
We introduce an acceleration for covariance matrix adaptation evolution strategies (CMA-ES) by means of adaptive diagonal decoding (dd-CMA). This diagonal acceleration endows the d…
Adaptive Ranking Based Constraint Handling for Explicitly Constrained Black-Box Optimization
Naoki Sakamoto, Youhei Akimoto
We propose a novel constraint-handling technique for the covariance matrix adaptation evolution strategy (CMA-ES). The proposed technique is aimed at solving explicitly constrained…
Sample Reuse via Importance Sampling in Information Geometric Optimization
Shinichi Shirakawa, Youhei Akimoto, Kazuki Ouchi +1
In this paper we propose a technique to reduce the number of function evaluations, which is often the bottleneck of the black-box optimization, in the information geometric optimiz…
Drift Theory in Continuous Search Spaces: Expected Hitting Time of the (1+1)-ES with 1/5 Success Rule
Youhei Akimoto, Anne Auger, Tobias Glasmachers
This paper explores the use of the standard approach for proving runtime bounds in discrete domains---often referred to as drift analysis---in the context of optimization on a cont…
Dynamic Optimization of Neural Network Structures Using Probabilistic Modeling
Shinichi Shirakawa, Yasushi Iwata, Youhei Akimoto
Deep neural networks (DNNs) are powerful machine learning models and have succeeded in various artificial intelligence tasks. Although various architectures and modules for the DNN…
Convergence of the Continuous Time Trajectories of Isotropic Evolution Strategies on Monotonic C^2-composite Functions
Youhei Akimoto, Anne Auger, Nikolaus Hansen
The Information-Geometric Optimization (IGO) has been introduced as a unified framework for stochastic search algorithms. Given a parametrized family of probability distributions o…
Theoretical foundation for CMA-ES from information geometric perspective
Youhei Akimoto, Yuichi Nagata, Isao Ono +1
This paper explores the theoretical basis of the covariance matrix adaptation evolution strategy (CMA-ES) from the information geometry viewpoint. To establish a theoretical founda…