1 citations · 1 across the 2 of their papers we have counts for
4 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…