DPP-HSS: Towards Fast and Scalable Hypervolume Subset Selection for Many-objective Optimization

Hypervolume subset selection (HSS) has received significant attention since it has a strong connection with evolutionary multi-objective optimization (EMO), such as environment selection and post-processing to identify representative solutions for decision-makers. The goal of HSS is to find the opti...

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Veröffentlicht in:	IEEE transactions on evolutionary computation S. 1
Hauptverfasser:	Gong, Cheng, Nan, Yang, Shang, Ke, Guo, Ping, Ishibuchi, Hisao, Zhang, Qingfu
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	IEEE 2024
Schlagworte:	Approximation algorithms Convergence Evolutionary computation evolutionary multi-objective optimization (EMO) Greedy algorithms Hypervolume subset selection Kernel many-objective optimization Optimization Pareto optimization Scalability Search problems Urban areas
ISSN:	1089-778X, 1941-0026
Online-Zugang:	Volltext
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