http://repub.eur.nl/res/aut/8272/ List of Publications en http://repub.eur.nl/static-eur/img/logo.png http://repub.eur.nl http://repub.eur.nl/res/pub/1655/ 2000-05-25T00:00:00Z We consider the Nelder and Mead Simplex Method for the optimization of stochastic simulation models. Existing and new adaptive extensions of the Nelder and Mead simplex method designed to improve the accuracy and consistency of the observed best point are studied. We compare the performance of the extensions on a small microsimulation model, as well as on five test functions. We found that gradually decreasing the noise during an optimization run is the most preferred approach for stochastic objective functions. The amount of computation effort needed for successful optimization is very sensitive to the timing of noise reduction and to the rate of decrease of the noise. Restarting the algorithm during the optimization run, in the sense that the algorithm applies a fresh simplex at certain iterations during an optimization run, has adverse effects in our tests for the microsimulation model and for most test functions. http://repub.eur.nl/res/pub/1647/ 2000-04-12T00:00:00Z We develop a framework for automated optimization of stochastic simulation models using Response Surface Methodology. The framework is especially intended for simulation models where the calculation of the corresponding stochastic response function is very expensive or time-consuming. Response Surface Methodology is frequently used for the optimization of stochastic simulation models in a non-automated fashion. In scientific applications there is a clear need for a standardized algorithm based on Response Surface Methodology. In addition, an automated algorithm is less time-consuming, since there is no need to interfere in the optimization process. In our framework for automated optimization we describe all choices that have to be made in constructing such an algorithm. http://repub.eur.nl/res/pub/1595/ 1999-07-28T00:00:00Z Microsimulation models are increasingly used in the evaluation of cancer screening. Latent parameters of such models can be estimated by optimization of the goodness-of-fit. We compared the efficiency and accuracy of the Response Surface Methodology and the Nelder and Mead Simplex Method for optimization of microsimulation models. To this end, we tested several automated versions of both methods on a small microsimulation model, as well as on a standard set of test functions. With respect to accuracy, Response Surface Methodology performed better in case of optimization of the microsimulation model, whereas the results for the test functions were rather variable. The Nelder and Mead Simplex Method performed more efficiently than Response Surface Methodology, both for the microsimulation model and the test functions.