5 citations · 6 across the 4 of their papers we have counts for
6 papers
Effects of Objective Normalization on Regions of Interest in Preference-Based Evolutionary Multi-Objective Optimization
Ryuichi Mogami, Ryoji Tanabe
Preference-based evolutionary multi-objective optimization (PBEMO) aims to approximate a region of interest (ROI) defined by the preference information from a decision maker (DM).…
Quantitative Performance Analysis of Stopping Criteria for CMA-ES
Ryoji Tanabe
Covariance matrix adaptation evolution strategy (CMA-ES) is a state-of-the-art black-box optimization algorithm. In general, CMA-ES uses a portfolio of multiple stopping criteria t…
Benchmarking Stopping Criteria for Evolutionary Multi-objective Optimization
Kenji Kitamura, Ryoji Tanabe
Stopping criteria automatically determine when to stop an evolutionary algorithm, so as not to waste function evaluations on a stagnant population. Although stopping criteria play…
Analyzing the Landscape of the Indicator-based Subset Selection Problem
Keisuke Korogi, Ryoji Tanabe
The indicator-based subset selection problem (ISSP) involves finding a point subset that minimizes or maximizes a quality indicator. The ISSP is frequently found in evolutionary mu…
On Constructing Algorithm Portfolios in Algorithm Selection for Computationally Expensive Black-box Optimization in the Fixed-budget Setting
Takushi Yoshikawa, Ryoji Tanabe
Feature-based offline algorithm selection has shown its effectiveness in a wide range of optimization problems, including the black-box optimization problem. An algorithm selection…
Benchmarking Parameter Control Methods in Differential Evolution for Mixed-Integer Black-Box Optimization
Ryoji Tanabe
Differential evolution (DE) generally requires parameter control methods (PCMs) for the scale factor and crossover rate. Although a better understanding of PCMs provides a useful c…