Metamodel-based design is a well-established method for providing fast and accurate approximations of expensive computer models to enable faster optimization and rapid design space exploration. Traditionally, a metamodel is developed by fitting a surface to a set of training points that are generated with an expensive computer model or simulation. A requirement of this process is that the function being approximated is continuous. However, many engineering problems have variables that are discrete and a function response that is discontinuous in nature. In this paper, a classifier-guided sampling method is presented that can be used for optimization and design space exploration of expensive computer models that have discrete variables and discontinuous responses. The method is tested on a set of example problems. Results show that the method significantly improves the rate of convergence towards known global optima, on average, when compared to random search.
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