Data-Driven Evolutionary Optimization: An Overview and Case Studies

Most evolutionary optimization algorithms assume that the evaluation of the objective and constraint functions is straightforward. In solving many real-world optimization problems, however, such objective functions may not exist. Instead, computationally expensive numerical simulations or costly phy...

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Veröffentlicht in:	IEEE transactions on evolutionary computation Jg. 23; H. 3; S. 442 - 458
Hauptverfasser:	Jin, Yaochu, Wang, Handing, Chugh, Tinkle, Guo, Dan, Miettinen, Kaisa
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.06.2019 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Computational modeling Computer simulation Data mining Data models Data science data-driven optimization Evolutionary algorithms evolutionary algorithms (EAs) Fitness Machine learning model management Optimization Sociology Statistics surrogate Taxonomy
ISSN:	1089-778X, 1941-0026
Online-Zugang:	Volltext
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