Detecting Hidden and Irrelevant Objectives in Interactive Multiobjective Optimization

Evolutionary multiobjective optimization algorithms (EMOAs) typically assume that all objectives that are relevant to the decision maker (DM) are optimized by the EMOA. In some scenarios, however, there are irrelevant objectives that are optimized by the EMOA but ignored by the DM, as well as, hidde...

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Veröffentlicht in:	IEEE transactions on evolutionary computation 2024-04, Vol.28 (2), p.544-557
Hauptverfasser:	Shavarani, Seyed Mahdi, Lopez-Ibanez, Manuel, Allmendinger, Richard
Format:	Artikel
Sprache:	eng
Schlagworte:	Benchmark testing Calcium Costs Dimension reduction Evolutionary algorithms Feature extraction feature selection hidden objectives interactive multiobjective optimization irrelevant objectives machine learning Multiple objective analysis Object recognition Objectives Optimization Pareto optimization
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