Memory-based Stochastic Optimization

In this paper we introduce new algorithms for optimizing noisy plants in which each experiment is very expensive. The algorithms build a global non-linear model of the expected output at the same time as using Bayesian linear regression analysis of locally weighted polynomial models. The local model answers queries about confi(cid:173) dence, noise, gradient and Hessians, and use them to make auto(cid:173) mated decisions similar to those made by a practitioner of Response Surface Methodology. The global and local models are combined naturally as a locally weighted regression. We examine the ques(cid:173) tion of whether the global model can really help optimization, and we extend it to the case of time-varying functions.