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...

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Vydané v:	Evolutionary intelligence Ročník 17; číslo 5-6; s. 3767 - 3791
Hlavní autori:	de Sousa, Mateus Clemente, Meneghini, Ivan Reinaldo, Guimarães, Frederico Gadelha
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Berlin/Heidelberg Springer Berlin Heidelberg 01.10.2024 Springer Nature B.V
Predmet:	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
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