"ARGESIM" . . . . "OPTIMIZATION OF MODELS: LOOKING FOR THE BEST STRATEGY"@en . "RIV/68407700:21230/07:03133126" . "P(KJB201210701), Z(MSM6840770012)" . . . "Optimalizace model\u016F: hled\u00E1n\u00ED nejlep\u0161\u00ED strategie"@cs . "314;320" . . . "[0B3ABE0E2ADE]" . "OPTIMIZATION OF MODELS: LOOKING FOR THE BEST STRATEGY" . . "3"^^ . . "439884" . . . . "\u0160norek, Miroslav" . "3"^^ . . "Kord\u00EDk, Pavel" . "978-3-901608-32-2" . . "Ljubljana" . "Vienna" . . "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 . . "OPTIMIZATION OF MODELS: LOOKING FOR THE BEST STRATEGY" . "Kov\u00E1\u0159\u00EDk, Oleg" . "7"^^ . . "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." . "Proceedings of the 6th EUROSIM Congress on Modelling and Simulation" . "V tomto \u010Dl\u00E1nku porovn\u00E1v\u00E1me v\u00FDsledky mnoha metod (gradientn\u00EDch, genetika, hejna) pro optimalizaci spojit\u00FDch parametr\u016F modelu. Navrhli jsme evolu\u010Dn\u00ED algoritmus, kter\u00FD vhodn\u00E9 optimaliza\u010Dn\u00ED metody vybere automaticky na z\u00E1klad\u011B charakteru dat. Prvn\u00ED v\u00FDsledky nazna\u010Duj\u00ED, \u017Ee se jedn\u00E1 o velmi slibn\u00FD p\u0159\u00EDstup."@cs . . . . "ACO; Conjugate Gradient Method; Differential Evolution; GAME; Genetic Algorithms; PSO; Quasi Newton method; optimization"@en . . . "Optimalizace model\u016F: hled\u00E1n\u00ED nejlep\u0161\u00ED strategie"@cs . . "RIV/68407700:21230/07:03133126!RIV08-AV0-21230___" . . . "2007-09-09+02:00"^^ . "21230" . "OPTIMIZATION OF MODELS: LOOKING FOR THE BEST STRATEGY"@en .