We propose a randomized search method called Stochastic Model Reference Adaptive
Search (SMRAS) for solving stochastic optimization problems in situations where the objective
functions cannot be evaluated exactly, but can be estimated with some noise (or uncertainty), e.g.,
via simulation. The method generalizes the recently proposed Model Reference Adaptive Search
(MRAS) for deterministic optimization, which is motivated by the well-known Cross-Entropy (CE)
method. We prove global convergence of SMRAS in a general stochastic setting, and carry out
numerical studies to illustrate its performance. An emphasis of this paper is on the application
of SMRAS for solving static stochastic optimization problems; its various applications for solving
dynamic decision making problems can be found in H. S. Chang, M. C. Fu, J. Hu, and S. I. Marcus, Simulation-based Algorithms for Markov
Decision Processes, Springer-Verlag, London, 2007.