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Statements

Subject Item
n2:RIV%2F67985807%3A_____%2F10%3A00351863%21RIV11-GA0-67985807
rdf:type
n6:Vysledek skos:Concept
dcterms:description
This paper presents an important real-world application of both evolutionary computation and learning, an application to the search for optimal catalytic materials. In this area, evolutionary and especially genetic algorithms are encountered most frequently. However, their application is far from any standard methodology, due to problems with mixed optimization and constraints. The paper describes how these difficulties are dealt with in the evolutionary optimization system GENACAT, recently developed for searching optimal catalysts. It also recalls that the costly evaluation of objective functions in this application area can be tackled through learning suitable regression models of those functions, called surrogate models. Ongoing integration of neural-networks-based surrogate modelling with GENACAT is illustrated on two brief examples. This paper presents an important real-world application of both evolutionary computation and learning, an application to the search for optimal catalytic materials. In this area, evolutionary and especially genetic algorithms are encountered most frequently. However, their application is far from any standard methodology, due to problems with mixed optimization and constraints. The paper describes how these difficulties are dealt with in the evolutionary optimization system GENACAT, recently developed for searching optimal catalysts. It also recalls that the costly evaluation of objective functions in this application area can be tackled through learning suitable regression models of those functions, called surrogate models. Ongoing integration of neural-networks-based surrogate modelling with GENACAT is illustrated on two brief examples.
dcterms:title
Evolutionary Optimization of Catalysts Assisted by Neural-Network Learning Evolutionary Optimization of Catalysts Assisted by Neural-Network Learning
skos:prefLabel
Evolutionary Optimization of Catalysts Assisted by Neural-Network Learning Evolutionary Optimization of Catalysts Assisted by Neural-Network Learning
skos:notation
RIV/67985807:_____/10:00351863!RIV11-GA0-67985807
n3:aktivita
n11:Z n11:P
n3:aktivity
P(GA201/08/0802), P(GEICC/08/E018), Z(AV0Z10300504)
n3:dodaniDat
n12:2011
n3:domaciTvurceVysledku
n4:6036627
n3:druhVysledku
n17:D
n3:duvernostUdaju
n9:S
n3:entitaPredkladatele
n21:predkladatel
n3:idSjednocenehoVysledku
258050
n3:idVysledku
RIV/67985807:_____/10:00351863
n3:jazykVysledku
n16:eng
n3:klicovaSlova
evolutionary optimization; mixed optimization; constrained optimization; neural network learning; surrogate modelling; evolutionary algorithms in catalysis
n3:klicoveSlovo
n10:mixed%20optimization n10:neural%20network%20learning n10:evolutionary%20optimization n10:surrogate%20modelling n10:evolutionary%20algorithms%20in%20catalysis n10:constrained%20optimization
n3:kontrolniKodProRIV
[C7A626AC2EFE]
n3:mistoKonaniAkce
Kanpur
n3:mistoVydani
Berlin
n3:nazevZdroje
Simulated Evolution and Learning
n3:obor
n15:IN
n3:pocetDomacichTvurcuVysledku
1
n3:pocetTvurcuVysledku
3
n3:projekt
n13:GA201%2F08%2F0802 n13:GEICC%2F08%2FE018
n3:rokUplatneniVysledku
n12:2010
n3:tvurceVysledku
Holeňa, Martin Rodemerck, U. Linke, D.
n3:typAkce
n20:WRD
n3:zahajeniAkce
2010-12-01+01:00
n3:zamer
n14:AV0Z10300504
s:numberOfPages
10
n18:hasPublisher
Springer-Verlag
n7:isbn
978-3-642-17297-7