Visualization and Performance Metric in Many-Objective Optimization

Visualization of population in a high-dimensional objective space throughout the evolution process presents an attractive feature that could be well exploited in designing many-objective evolutionary algorithms (MaOEAs). In this paper, a new visualization method is proposed. It maps individuals from...

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Veröffentlicht in:	IEEE transactions on evolutionary computation Jg. 20; H. 3; S. 386 - 402
Hauptverfasser:	Zhenan He, Yen, Gary G.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.06.2016 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Approximation Approximation methods Dominance Evolutionary algorithms Evolutionary design method Heating many-objective evolutionary algorithm many-objective optimization problem mapping Measurement Pareto optimality Performance measurement performance metric Quality assessment Shape Sociology Statistics Visualization performance metric mapping visualization many-objective optimization problem (MaOP) Many-objective evolutionary algorithm (MaOEA)
ISSN:	1089-778X, 1941-0026
Online-Zugang:	Volltext
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