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Statements

Subject Item
n2:RIV%2F68407700%3A21230%2F07%3A03133126%21RIV08-AV0-21230___
rdf:type
skos:Concept n12:Vysledek
dcterms:description
When parameters of model are being adjusted, model is learning to mimic the behaviour of a real world system. Optimization methods are responsible for parameters adjustment. The problem is that each real world system is different and its model should be of different complexity. It is almost impossible to decide which optimization method will perform the best (optimally adjust parameters of the model). In this paper we compare the performance of several methods for nonlinear parameters optimization. The gradient based methods such as Quasi-Newton or Conjugate Gradient are compared to several nature inspired methods. We designed an evolutionary algorithm selecting the best optimization methods for models of various complexity. Our experiments proved that the evolution of optimization methods for particular problems is very promising approach. When parameters of model are being adjusted, model is learning to mimic the behaviour of a real world system. Optimization methods are responsible for parameters adjustment. The problem is that each real world system is different and its model should be of different complexity. It is almost impossible to decide which optimization method will perform the best (optimally adjust parameters of the model). In this paper we compare the performance of several methods for nonlinear parameters optimization. The gradient based methods such as Quasi-Newton or Conjugate Gradient are compared to several nature inspired methods. We designed an evolutionary algorithm selecting the best optimization methods for models of various complexity. Our experiments proved that the evolution of optimization methods for particular problems is very promising approach. V tomto článku porovnáváme výsledky mnoha metod (gradientních, genetika, hejna) pro optimalizaci spojitých parametrů modelu. Navrhli jsme evoluční algoritmus, který vhodné optimalizační metody vybere automaticky na základě charakteru dat. První výsledky naznačují, že se jedná o velmi slibný přístup.
dcterms:title
Optimalizace modelů: hledání nejlepší strategie OPTIMIZATION OF MODELS: LOOKING FOR THE BEST STRATEGY OPTIMIZATION OF MODELS: LOOKING FOR THE BEST STRATEGY
skos:prefLabel
OPTIMIZATION OF MODELS: LOOKING FOR THE BEST STRATEGY OPTIMIZATION OF MODELS: LOOKING FOR THE BEST STRATEGY Optimalizace modelů: hledání nejlepší strategie
skos:notation
RIV/68407700:21230/07:03133126!RIV08-AV0-21230___
n5:strany
314;320
n5:aktivita
n16:P n16:Z
n5:aktivity
P(KJB201210701), Z(MSM6840770012)
n5:dodaniDat
n14:2008
n5:domaciTvurceVysledku
n9:9608362 n9:1266500 n9:7035586
n5:druhVysledku
n7:D
n5:duvernostUdaju
n18:S
n5:entitaPredkladatele
n19:predkladatel
n5:idSjednocenehoVysledku
439884
n5:idVysledku
RIV/68407700:21230/07:03133126
n5:jazykVysledku
n6:eng
n5:klicovaSlova
ACO; Conjugate Gradient Method; Differential Evolution; GAME; Genetic Algorithms; PSO; Quasi Newton method; optimization
n5:klicoveSlovo
n11:Genetic%20Algorithms n11:Conjugate%20Gradient%20Method n11:ACO n11:PSO n11:Differential%20Evolution n11:Quasi%20Newton%20method n11:optimization n11:GAME
n5:kontrolniKodProRIV
[0B3ABE0E2ADE]
n5:mistoKonaniAkce
Ljubljana
n5:mistoVydani
Vienna
n5:nazevZdroje
Proceedings of the 6th EUROSIM Congress on Modelling and Simulation
n5:obor
n13:IN
n5:pocetDomacichTvurcuVysledku
3
n5:pocetTvurcuVysledku
3
n5:projekt
n8:KJB201210701
n5:rokUplatneniVysledku
n14:2007
n5:tvurceVysledku
Šnorek, Miroslav Kordík, Pavel Kovářík, Oleg
n5:typAkce
n21:WRD
n5:zahajeniAkce
2007-09-09+02:00
n5:zamer
n20:MSM6840770012
s:numberOfPages
7
n3:hasPublisher
ARGESIM
n15:isbn
978-3-901608-32-2
n22:organizacniJednotka
21230