Boosted Regression Forest for the Doubly Trained Surrogate Covariance Matrix Adaptation Evolution Strategy

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SYSNO	0494112
Title	Boosted Regression Forest for the Doubly Trained Surrogate Covariance Matrix Adaptation Evolution Strategy
Author(s)	Pitra, Zbyněk (UIVT-O)_{RID, ORCID, SAI} Repický, Jakub (UIVT-O)_{ORCID, SAI} Holeňa, Martin (UIVT-O)_{SAI, RID}
Source Title	ITAT 2018: Information Technologies – Applications and Theory. Proceedings of the 18th conference ITAT 2018. S. 72-79. - Aachen : Technical University & CreateSpace Independent Publishing Platform, 2018 / Krajči S.
Conference	ITAT 2018. Conference on Information Technologies – Applications and Theory /18./, 21.09.2018 - 25.09.2018, Plejsy
Document Type	Konferenční příspěvek (zahraniční konf.)
Grant	GA17-01251S GA ČR - Czech Science Foundation (CSF)
	SGS17/193/OHK4/3T/14, CZ - Czech Republic
	LM2015042, CZ - Czech Republic
Institutional support	UIVT-O - RVO:67985807
Language	eng
Country	DE
Keywords	Gradient boosting * Random forest * Black-box optimization * Surrogate model * Benchmarking
URL	http://ceur-ws.org/Vol-2203/72.pdf
Permanent Link	http://hdl.handle.net/11104/0287361

	File	Download	Size	Commentary	Version	Access
	0494112a.pdf	8	1.1 MB		Publisher’s postprint	require

Number of the records: 1