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Statements

Subject Item
n2:RIV%2F67985807%3A_____%2F12%3A00384877%21RIV13-GA0-67985807
rdf:type
skos:Concept n3:Vysledek
dcterms:description
Many today’s engineering tasks use approximation of their expensive objective function. Surrogate models, which are frequently used for this purpose, can save significant costs by substituting some of the experimental evaluations or simulations needed to achieve an optimal or near-optimal solution. This paper presents a surrogate model based on RBF networks. In contrast to the most of the surrogate models in the current literature, it can be directly used for problems with mixed continuous and discrete variables - clustering and generalized linear models are employed for dealing with discrete covariates. The model has been tested on a benchmark optimization problem and its approximation properties are presented on a real-world application data. Many today’s engineering tasks use approximation of their expensive objective function. Surrogate models, which are frequently used for this purpose, can save significant costs by substituting some of the experimental evaluations or simulations needed to achieve an optimal or near-optimal solution. This paper presents a surrogate model based on RBF networks. In contrast to the most of the surrogate models in the current literature, it can be directly used for problems with mixed continuous and discrete variables - clustering and generalized linear models are employed for dealing with discrete covariates. The model has been tested on a benchmark optimization problem and its approximation properties are presented on a real-world application data.
dcterms:title
RBF-based surrogate model for evolutionary optimization RBF-based surrogate model for evolutionary optimization
skos:prefLabel
RBF-based surrogate model for evolutionary optimization RBF-based surrogate model for evolutionary optimization
skos:notation
RIV/67985807:_____/12:00384877!RIV13-GA0-67985807
n3:predkladatel
n4:ico%3A67985807
n5:aktivita
n6:P n6:I n6:S
n5:aktivity
I, P(GA201/08/0802), S
n5:dodaniDat
n20:2013
n5:domaciTvurceVysledku
n13:9282149 n13:6036627
n5:druhVysledku
n18:D
n5:duvernostUdaju
n12:S
n5:entitaPredkladatele
n8:predkladatel
n5:idSjednocenehoVysledku
164171
n5:idVysledku
RIV/67985807:_____/12:00384877
n5:jazykVysledku
n17:eng
n5:klicovaSlova
surrogate models; mixed-variable optimization; rbf networks; evolutionary optimization
n5:klicoveSlovo
n9:evolutionary%20optimization n9:rbf%20networks n9:surrogate%20models n9:mixed-variable%20optimization
n5:kontrolniKodProRIV
[2DAD5B1A0395]
n5:mistoKonaniAkce
Ždiar
n5:mistoVydani
Seňa
n5:nazevZdroje
Information Technologies - Applications and Theory
n5:obor
n19:IN
n5:pocetDomacichTvurcuVysledku
2
n5:pocetTvurcuVysledku
2
n5:projekt
n21:GA201%2F08%2F0802
n5:rokUplatneniVysledku
n20:2012
n5:tvurceVysledku
Bajer, Lukáš Holeňa, Martin
n5:typAkce
n7:EUR
n5:zahajeniAkce
2012-09-17+02:00
s:numberOfPages
6
n15:hasPublisher
PONT s.r.o.
n16:isbn
978-80-971144-0-4