【Abstract】Random search is an important category of algorithms to solve continuous optimization via simulation problems. To design an efficient random search algorithm, the handling of the triple “E” (i.e., exploration, exploitation and estimation) is critical. The first two E’s refer to the design of sampling distribution to balance explorative and exploitative searches, whereas the third E refers to the estimation of objective function values based on noisy simulation observations. In this paper, we propose a class of Gaussian process-based random search (GPRS) algorithms, which provide a new framework to handle the triple “E.” In each iteration, algorithms under the framework build a Gaussian process surrogate model to estimate the objective function based on single observation of each sampled solution and randomly sample solutions from a lower-bounded sampling distribution.