1 citations · 1 across the 1 of their papers we have counts for
2 papers
cs.NE2026
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).…
cs.NE2026★ 1 cited
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…