To solve experimental optimization tasks, design of experiments (DOE) methods are frequently employed. DOE methods provide general strategies for planning data collection and for modeling experimental output. In this context we refer to the experimental output being modeled as the design target, or target for short, and the range of processing conditions being considered as the design space. Since laboratory-based experiments are often time consuming and costly, DOE also strives for efficient sampling of the design space. Conventional approaches to DOE include space-filling designs, factorial designs , and response surface methods , where the latter category is frequently regarded as an industry standard. A shortcoming of many conventional DOE approaches is the predetermined fashion in which they sample design space, which does not allow for information from new measurements to be utilized for improving the sampling strategy. Predetermined sampling strategies also tend to sample excessively in regions where the design target is relatively uniform, and insufficiently in regions with significant fluctuations.