A Set-Based Genetic Algorithm for Interval Many-Objective Optimization Problems

Interval many-objective optimization problems (IMaOPs), involving more than three objectives and at least one subjected to interval uncertainty, are ubiquitous in real-world applications. However, there have been very few effective methods for solving these problems. In this paper, we proposed a set...

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Veröffentlicht in:	IEEE transactions on evolutionary computation Jg. 22; H. 1; S. 47 - 60
Hauptverfasser:	Gong, Dunwei, Sun, Jing, Miao, Zhuang
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	IEEE 01.02.2018
Schlagworte:	Context Convergence Genetic algorithm Genetic algorithms interval many-objective optimization Noise measurement Optimization set-based evolution Sorting Uncertainty
ISSN:	1089-778X, 1941-0026
Online-Zugang:	Volltext
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