A clustering-based coevolutionary multi-objective evolutionary algorithm for handling robust and noisy optimization

The presence of uncertainty is commonplace in real-world scenarios. Uncertainties can be present in both the objective space and the decision space in optimization problems. These uncertainties can pose significant challenges for evolutionary algorithms. For example, perturbations in decision variab...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	Evolutionary intelligence Jg. 17; H. 5-6; S. 3767 - 3791
Hauptverfasser:	de Sousa, Mateus Clemente, Meneghini, Ivan Reinaldo, Guimarães, Frederico Gadelha
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Berlin/Heidelberg Springer Berlin Heidelberg 01.10.2024 Springer Nature B.V
Schlagworte:	Applications of Mathematics Artificial Intelligence Bioinformatics Clustering Control Decision making Engineering Evolutionary algorithms Function generators Genetic algorithms Mathematical and Computational Engineering Mechatronics Multiple objective analysis Noise levels Optimization Pareto optimum Research Paper Robotics Robustness (mathematics) Statistical Physics and Dynamical Systems Uncertainty Variables Multi-objective optimization Robust optimization Noisy optimization Clustering Evolutionary algorithm
ISSN:	1864-5909, 1864-5917
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!