Abstract: A design optimization method based on active learning, which involves dynamic exploration and exploitation of the design space of interest using an ensemble of machine learning algorithms. In this approach, a hybrid methodology incorporating an explorative weak learner which fits high-level information about the response surface, and an exploitative strong learner (based on committee machine) that fits finer details around promising regions identified by the weak learner, is employed. For each design iteration, an aristocratic approach is used to select a set of nominees, where points that meet a threshold merit value as predicted by the weak learner are selected to be evaluated using function evaluation. In addition to these points, the global optimum as predicted by the strong learner is also evaluated to enable rapid convergence to the actual global optimum once the most promising region has been identified by the optimizer.