RT Journal Article
T1 Efficient dynamic resampling for dominance-based multiobjective evolutionary optimization
A1 Cervantes, Alejandro
A1 Quintana, David
A1 Recio, Gustavo
AB Multi-objective optimization problems are often subject to the presence of objectives that require expensive resampling for their computation. This is the case for many robustness metrics, which are frequently used as an additional objective that accounts for the reliability of specific sections of the solution space. Typical robustness measurements use resampling, but the number of samples that constitute a precise dispersion measure has a potentially large impact on the computational cost of an algorithm. This article proposes the integration of dominance based statistical testing methods as part of the selection mechanism of evolutionary multi-objective genetic algorithms with the aim of reducing the number of fitness evaluations. The performance of the approach is tested on five classical benchmark functions integrating it into two well-known algorithms, NSGA-II and SPEA2. The experimental results show a significant reduction in the number of fitness evaluations while, at the same time, maintaining the quality of the solutions.
PB Taylor & Francis
SN 0305-215X (print)
SN 1029-0273 (online)
YR 2016
FD 2016-06-22
LK https://hdl.handle.net/10016/23234
UL https://hdl.handle.net/10016/23234
LA eng
NO The authors acknowledge financial support granted by the Spanish Ministry of Economy and Competitivity under grant ENE2014-56126-C2-2-R.
DS e-Archivo
RD 2 may. 2024