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Statements

Subject Item
n2:RIV%2F67985807%3A_____%2F12%3A00384885%21RIV13-GA0-67985807
rdf:type
skos:Concept n10:Vysledek
dcterms:description
Evolutionary optimization is often applied to problems, where simulations or experiments used as the fitness function are expensive to run. In such cases, surrogate models are used to reduce the number of fitness evaluations. Some of the problems also require a fixed size batch of solutions to be evaluated at a time. Traditional methods of selecting individuals for true evaluation to improve the surrogate model either require individual points to be evaluated, or couple the batch size with the EA generation size. We propose a queue based method for individual selection based on active learning of a kriging model. Individuals are selected using the confidence intervals predicted by the model, added to a queue and evaluated once the queue length reaches the batch size. The method was tested on several standard benchmark problems. Results show that the proposed algorithm is able to achieve a solution using significantly less evaluations of the true fitness function. The effect of the batc Evolutionary optimization is often applied to problems, where simulations or experiments used as the fitness function are expensive to run. In such cases, surrogate models are used to reduce the number of fitness evaluations. Some of the problems also require a fixed size batch of solutions to be evaluated at a time. Traditional methods of selecting individuals for true evaluation to improve the surrogate model either require individual points to be evaluated, or couple the batch size with the EA generation size. We propose a queue based method for individual selection based on active learning of a kriging model. Individuals are selected using the confidence intervals predicted by the model, added to a queue and evaluated once the queue length reaches the batch size. The method was tested on several standard benchmark problems. Results show that the proposed algorithm is able to achieve a solution using significantly less evaluations of the true fitness function. The effect of the batc
dcterms:title
Evolutionary optimization with active learning of surrogate models and fixed evaluation batch size Evolutionary optimization with active learning of surrogate models and fixed evaluation batch size
skos:prefLabel
Evolutionary optimization with active learning of surrogate models and fixed evaluation batch size Evolutionary optimization with active learning of surrogate models and fixed evaluation batch size
skos:notation
RIV/67985807:_____/12:00384885!RIV13-GA0-67985807
n10:predkladatel
n11:ico%3A67985807
n3:aktivita
n4:I n4:S n4:P
n3:aktivity
I, P(GA201/08/0802), S
n3:dodaniDat
n14:2013
n3:domaciTvurceVysledku
n12:6036627
n3:druhVysledku
n13:D
n3:duvernostUdaju
n21:S
n3:entitaPredkladatele
n7:predkladatel
n3:idSjednocenehoVysledku
135326
n3:idVysledku
RIV/67985807:_____/12:00384885
n3:jazykVysledku
n15:eng
n3:klicovaSlova
evolutionary optimization; fitness evaluation; surrogate modelling; Gaussian process; active learning
n3:klicoveSlovo
n5:surrogate%20modelling n5:Gaussian%20process n5:evolutionary%20optimization n5:active%20learning n5:fitness%20evaluation
n3:kontrolniKodProRIV
[49C7BB6CA6A7]
n3:mistoKonaniAkce
Ždiar
n3:mistoVydani
Seňa
n3:nazevZdroje
Information Technologies - Applications and Theory
n3:obor
n17:IN
n3:pocetDomacichTvurcuVysledku
1
n3:pocetTvurcuVysledku
2
n3:projekt
n18:GA201%2F08%2F0802
n3:rokUplatneniVysledku
n14:2012
n3:tvurceVysledku
Charypar, V. Holeňa, Martin
n3:typAkce
n8:EUR
n3:zahajeniAkce
2012-09-17+02:00
s:numberOfPages
8
n20:hasPublisher
PONT s.r.o.
n9:isbn
978-80-971144-0-4