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Statements

Subject Item
n2:RIV%2F67985807%3A_____%2F12%3A00380963%21RIV13-GA0-67985807
rdf:type
n15:Vysledek skos:Concept
dcterms:description
The search for best performing catalysts leads to high-dimensional optimization tasks. They are by far most frequently tackled using evolutionary algorithms, usually implemented in systems developed specifically for the area of catalysis. Their fitness functions are black-box functions with costly and time-consuming empirical evaluation. This suggests to apply surrogate modeling. The paper points out three difficulties challenging the application of surrogate modeling to catalysts optimization: mixed-variables optimization, assessing the suitability of different models, and scalarization of multiple objectives. It then provides examples of how those challenges are tackled in real-world catalysts optimization tasks. The examples are based on results obtained in three such tasks using one of specific evolutionary optimization systems for catalysis. The search for best performing catalysts leads to high-dimensional optimization tasks. They are by far most frequently tackled using evolutionary algorithms, usually implemented in systems developed specifically for the area of catalysis. Their fitness functions are black-box functions with costly and time-consuming empirical evaluation. This suggests to apply surrogate modeling. The paper points out three difficulties challenging the application of surrogate modeling to catalysts optimization: mixed-variables optimization, assessing the suitability of different models, and scalarization of multiple objectives. It then provides examples of how those challenges are tackled in real-world catalysts optimization tasks. The examples are based on results obtained in three such tasks using one of specific evolutionary optimization systems for catalysis.
dcterms:title
Surrogate Modeling in the Evolutionary Optimization of Catalytic Materials Surrogate Modeling in the Evolutionary Optimization of Catalytic Materials
skos:prefLabel
Surrogate Modeling in the Evolutionary Optimization of Catalytic Materials Surrogate Modeling in the Evolutionary Optimization of Catalytic Materials
skos:notation
RIV/67985807:_____/12:00380963!RIV13-GA0-67985807
n15:predkladatel
n17:ico%3A67985807
n3:aktivita
n7:P n7:I
n3:aktivity
I, P(GA201/08/0802), P(GAP202/11/1368)
n3:dodaniDat
n5:2013
n3:domaciTvurceVysledku
n8:9282149 n8:6036627
n3:druhVysledku
n21:D
n3:duvernostUdaju
n11:S
n3:entitaPredkladatele
n13:predkladatel
n3:idSjednocenehoVysledku
172601
n3:idVysledku
RIV/67985807:_____/12:00380963
n3:jazykVysledku
n18:eng
n3:klicovaSlova
evolutionary optimization; mixed optimization; surrogate modeling; model suitability; applications in chemistry
n3:klicoveSlovo
n9:surrogate%20modeling n9:model%20suitability n9:evolutionary%20optimization n9:applications%20in%20chemistry n9:mixed%20optimization
n3:kontrolniKodProRIV
[E1C5CB26E13A]
n3:mistoKonaniAkce
Philadelphia
n3:mistoVydani
New York
n3:nazevZdroje
GECCO '12. Proceedings of the fourteenth international conference on Genetic and evolutionary computation conference
n3:obor
n4:IN
n3:pocetDomacichTvurcuVysledku
2
n3:pocetTvurcuVysledku
3
n3:projekt
n14:GAP202%2F11%2F1368 n14:GA201%2F08%2F0802
n3:rokUplatneniVysledku
n5:2012
n3:tvurceVysledku
Linke, D. Bajer, Lukáš Holeňa, Martin
n3:typAkce
n19:WRD
n3:wos
000309611100137
n3:zahajeniAkce
2012-07-07+02:00
s:numberOfPages
8
n12:hasPublisher
ACM
n20:isbn
978-1-4503-1177-9