Reference Set Generator: A Method for Pareto Front Approximation and Reference Set Generation DOI Creative Commons
Angel E. Rodríguez-Fernandez, Hao Wang,

Oliver Schütze

et al.

Mathematics, Journal Year: 2025, Volume and Issue: 13(10), P. 1626 - 1626

Published: May 15, 2025

In this paper, we address the problem of obtaining bias-free and complete finite size approximations solution sets (Pareto fronts) multi-objective optimization problems (MOPs). Such are, in particular, required for fair usage distance-based performance indicators, which are frequently used evolutionary (EMO). If Pareto front biased or incomplete, use these indicators can lead to misleading false information. To issue, propose Reference Set Generator (RSG), can, principle, be applied fronts any shape dimension. We finally demonstrate strength novel approach on several benchmark problems.

Language: Английский

Neural architecture search for microscopic image segmentation using a constrained multi-objective evolutionary algorithm DOI
Wei Wang, Xianpeng Wang, Zhiming Dong

et al.

Engineering Optimization, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 20

Published: April 14, 2025

Language: Английский

Citations

0
Reference Set Generator: A Method for Pareto Front Approximation and Reference Set Generation DOI Creative Commons
Angel E. Rodríguez-Fernandez, Hao Wang,

Oliver Schütze

et al.

Mathematics, Journal Year: 2025, Volume and Issue: 13(10), P. 1626 - 1626

Published: May 15, 2025

In this paper, we address the problem of obtaining bias-free and complete finite size approximations solution sets (Pareto fronts) multi-objective optimization problems (MOPs). Such are, in particular, required for fair usage distance-based performance indicators, which are frequently used evolutionary (EMO). If Pareto front biased or incomplete, use these indicators can lead to misleading false information. To issue, propose Reference Set Generator (RSG), can, principle, be applied fronts any shape dimension. We finally demonstrate strength novel approach on several benchmark problems.

Language: Английский

Citations

0