Combining Gaussian Processes and Neural Networks in Surrogate Modeling for Covariance Matrix Adaptation Evolution Strategy

Number of the records: 1

1.

SYSNO	0546157
Title	Combining Gaussian Processes and Neural Networks in Surrogate Modeling for Covariance Matrix Adaptation Evolution Strategy
Author(s)	Koza, J. (CZ) Tumpach, J. (CZ) Pitra, Z. (CZ) Holeňa, Martin (UIVT-O)_{SAI, RID}
Source Title	Proceedings of the 21st Conference Information Technologies – Applications and Theory (ITAT 2021). S. 29-38. - Aachen : Technical University & CreateSpace Independent Publishing, 2021 / Brejová B. ; Ciencialová L. ; Holeňa M. ; Mráz F. ; Pardubská D. ; Plátek M. ; Vinař T.
Conference	ITAT 2021: Information Technologies - Applications and Theory /21./, 24.09.2021 - 28.09.2021, Heľpa
Document Type	Konferenční příspěvek (zahraniční konf.)
Grant	GA18-18080S GA ČR - Czech Science Foundation (CSF), CZ - Czech Republic
	LM2018140, CZ - Czech Republic
Institutional support	UIVT-O - RVO:67985807
Language	eng
Country	DE
Keywords	black-box optimization * surrogate modeling * artificial neural networks * Gaussian processes * covariance functions
Cooperating institutions	Fakulta informačních technologií ČVUT (Czech Republic) FJFI ČVUT Praha (Czech Republic) Matematicko-fyzikalni fakulta UK
URL	http://ics.upjs.sk/~antoni/ceur-ws.org/Vol-0000/paper27.pdf
Permanent Link	http://hdl.handle.net/11104/0322706

	File	Download	Size	Commentary	Version	Access
	0546157-aoa.pdf	2	1.7 MB	OA CC BY 4.0	Publisher’s postprint	open-access

Number of the records: 1