Multiple surrogates for prediction and optimization

Statistical modeling of computer experiments embraces the set of methodologies for generating a surrogate model (also known as metamodel or response surface approximation) used to replace an expensive simulation code. The aim of surrogate modeling is to construct an approximation of a response of interest based on a limited number of expensive simulations. Nevertheless, after years of intensive research on the field, surrogate-based analysis and optimization is still a struggle to achieve maximum accuracy for a given number of simulations.;In this dissertation, we have taken advantage of multiple surrogates to address the issues that we face when we (i) want to build an accurate surrogate model under limited computational budget, (ii) use the surrogate for constrained optimization and the exact analysis shows that the solution is infeasible, and (iii) use the surrogate for global optimization and do not know where to place a set of points in which we are most likely to have improvement.;In terms of prediction accuracy, we have found that multiple surrogates work as insurance against poorly fitted models. Additionally, we propose the use of safety margins to conservatively compensate for fitting errors associated with surrogates. We were able to estimate the safety margin for a specific conservativeness level, and we found that it is possible to select a surrogate with the best compromise between conservativeness and loss of accuracy.;In terms of optimization, we proposed two strategies for enabling surrogate-based global optimization with parallel function evaluations. The first one is based on the simultaneous use of multiple surrogates (a set of surrogates collaboratively provide multiple points). The second strategy uses a single surrogate and one cheap to evaluate criterion (probability of improvement) for multiple point selection approximation. In both cases, we found that we could successfully speed up the optimization convergence without clear penalties as far as number of function evaluations. (Full text of this dissertation may be available via the University of Florida Libraries web site. Please check http://www.uflib.ufl.edu/etd.html).