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Combining Gaussian Processes and Neural Networks in Surrogate Modeling for Covariance Matrix Adaptation Evolution Strategy
SYS 0546157 LBL 01000a^^22220027750^450 005 20231122145955.7 014 $a 85116711806 $2 SCOPUS 017 $2 DOI 100 $a 20211004d m y slo 03 ba 101 $a eng 102 $a DE 200 1-
$a Combining Gaussian Processes and Neural Networks in Surrogate Modeling for Covariance Matrix Adaptation Evolution Strategy 215 $a 10 s. $c E 463 -1
$1 001 cav_un_epca*0546156 $1 011 $a 1613-0073 $1 200 1 $a Proceedings of the 21st Conference Information Technologies – Applications and Theory (ITAT 2021) $v S. 29-38 $1 210 $a Aachen $c Technical University & CreateSpace Independent Publishing $d 2021 $1 702 1 $a Brejová $b B. $4 340 $1 702 1 $a Ciencialová $b L. $4 340 $1 702 1 $a Holeňa $b M. $4 340 $1 702 1 $a Mráz $b F. $4 340 $1 702 1 $a Pardubská $b D. $4 340 $1 702 1 $a Plátek $b M. $4 340 $1 702 1 $a Vinař $b T. $4 340 610 $a black-box optimization 610 $a surrogate modeling 610 $a artificial neural networks 610 $a Gaussian processes 610 $a covariance functions 700 -1
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$3 cav_un_auth*0383703 $a Tumpach $b J. $y CZ 701 -1
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$3 cav_un_auth*0100761 $a Holeňa $b Martin $p UIVT-O $i Oddělení strojového učení $j Department of Machine Learning $w Department of Machine Learning $T Ústav informatiky AV ČR, v. v. i. 856 $u http://ics.upjs.sk/~antoni/ceur-ws.org/Vol-0000/paper27.pdf $9 RIV
Number of the records: 1