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rdf:type
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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. (en)
- 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. (cs)
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Title
| - OPTIMIZATION OF MODELS: LOOKING FOR THE BEST STRATEGY
- Optimalizace modelů: hledání nejlepší strategie (cs)
- OPTIMIZATION OF MODELS: LOOKING FOR THE BEST STRATEGY (en)
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skos:prefLabel
| - OPTIMIZATION OF MODELS: LOOKING FOR THE BEST STRATEGY
- Optimalizace modelů: hledání nejlepší strategie (cs)
- OPTIMIZATION OF MODELS: LOOKING FOR THE BEST STRATEGY (en)
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skos:notation
| - RIV/68407700:21230/07:03133126!RIV08-AV0-21230___
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http://linked.open.../vavai/riv/strany
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http://linked.open...avai/riv/aktivita
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http://linked.open...avai/riv/aktivity
| - P(KJB201210701), Z(MSM6840770012)
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http://linked.open...vai/riv/dodaniDat
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http://linked.open...aciTvurceVysledku
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http://linked.open.../riv/druhVysledku
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http://linked.open...iv/duvernostUdaju
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http://linked.open...titaPredkladatele
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http://linked.open...dnocenehoVysledku
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http://linked.open...ai/riv/idVysledku
| - RIV/68407700:21230/07:03133126
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http://linked.open...riv/jazykVysledku
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http://linked.open.../riv/klicovaSlova
| - ACO; Conjugate Gradient Method; Differential Evolution; GAME; Genetic Algorithms; PSO; Quasi Newton method; optimization (en)
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http://linked.open.../riv/klicoveSlovo
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http://linked.open...ontrolniKodProRIV
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http://linked.open...v/mistoKonaniAkce
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http://linked.open...i/riv/mistoVydani
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http://linked.open...i/riv/nazevZdroje
| - Proceedings of the 6th EUROSIM Congress on Modelling and Simulation
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http://linked.open...in/vavai/riv/obor
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http://linked.open...ichTvurcuVysledku
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http://linked.open...cetTvurcuVysledku
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http://linked.open...vavai/riv/projekt
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http://linked.open...UplatneniVysledku
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http://linked.open...iv/tvurceVysledku
| - Kordík, Pavel
- Kovářík, Oleg
- Šnorek, Miroslav
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http://linked.open...vavai/riv/typAkce
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http://linked.open.../riv/zahajeniAkce
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http://linked.open...n/vavai/riv/zamer
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number of pages
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http://purl.org/ne...btex#hasPublisher
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https://schema.org/isbn
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http://localhost/t...ganizacniJednotka
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is http://linked.open...avai/riv/vysledek
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