  Computer experiments with both qualitative and quantitative factors are
widely used in many applications. Motivated by the emerging need of optimal
configuration in the high-performance computing (HPC) system, this work
proposes a sequential design, denoted as adaptive composite exploitation and
exploration (CEE), for optimization of computer experiments with qualitative
and quantitative factors. The proposed adaptive CEE method combines the
predictive mean and standard deviation based on the additive Gaussian process
to achieve a meaningful balance between exploitation and exploration for
optimization. Moreover, the adaptiveness of the proposed sequential procedure
allows the selection of next design point from the adaptive design region.
Theoretical justification of the adaptive design region is provided. The
performance of the proposed method is evaluated by several numerical examples
in simulations. The case study of HPC performance optimization further
elaborates the merits of the proposed method.
