Scalable GP with hyperparameters sharing based on transfer learning for solving expensive optimization problems

Surrogates are essential in surrogate-assisted evolutionary algorithms (SAEAs) for solving expensive optimization problems. Gaussian processes (GPs) are often used as surrogates for their accuracy in prediction and ability to quantify prediction uncertainty. However, calculating the inverse and dete...
Ausführliche Beschreibung

Gespeichert in:
Autor*in:

Hu, Caie [verfasserIn]

Zeng, Sanyou [verfasserIn]

Li, Changhe [verfasserIn]

Format:

E-Artikel

Sprache:

Englisch

Erschienen:

2023

Schlagwörter:

Evolutionary computation

Expensive optimization

Gaussian process

Transfer learning

Übergeordnetes Werk:

Enthalten in: Applied soft computing - Amsterdam [u.a.] : Elsevier Science, 2001, 148

Übergeordnetes Werk:

volume:148

DOI / URN:

10.1016/j.asoc.2023.110866

Katalog-ID:

ELV065665791

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