01457nab a2200193 4500 20260608201743.0 260224s2013 xxu ing Inglés Feraille, M. 37945 An Optimization Strategy Based on the Maximization of Matching-Targets' Probability for Unevaluated Results ene./feb. 2013 25/10/2013 ; 25/10/2013 11 p. ; 545-556 Transcripción del resumen del autor: The Maximization of Matching-Targets' Probability for Unevaluated Results (MMTPUR), technique presented in this paper, is based on the classical probabilistic optimization framework. The numerical function values that have not been evaluated are considered as stochastic functions. Thus, a Gaussian process uncertainty model is built for each required numerical function result (i.e., associated with each specified target) and is used to estimate probability density functions for unevaluated results. Parameter posterior distributions, used within the optimization process, then take into account these probabilities. This approach is particularly adapted when, getting one evaluation of the numerical function is very time consuming. In this paper, we provide a detailed outline of this technique. Finally, several test cases are developed to stress its potential. 1 ARTICULO 187938 187938 0 0 0 0 255757 BAAB BAAB 2026-03-06 200060092 0 200060092 2026-03-06 00:17:57 2026-03-06 ARTICULO