  Compared with the fixed-run designs, the sequential adaptive designs (SAD)
are thought to be more efficient and effective. Efficient global optimization
(EGO) is one of the most popular SAD methods for expensive black-box
optimization problems. A well-recognized weakness of the original EGO in
complex computer experiments is that it is serial, and hence the modern
parallel computing techniques cannot be utilized to speed up the running of
simulator experiments. For those multiple points EGO methods, the heavy
computation and points clustering are the obstacles. In this work, a novel
batch SAD method, named "accelerated EGO", is forwarded by using a refined
sampling/importance resampling (SIR) method to search the points with large
expected improvement (EI) values. The computation burden of the new method is
much lighter, and the points clustering is also avoided. The efficiency of the
proposed SAD is validated by nine classic test functions with dimension from 2
to 12. The empirical results show that the proposed algorithm indeed can
parallelize original EGO, and gain much improvement compared against the other
parallel EGO algorithm especially under high-dimensional case. Additionally, we
also apply the new method to the hyper-parameter tuning of Support Vector
Machine (SVM). Accelerated EGO obtains comparable cross validation accuracy
with other methods and the CPU time can be reduced a lot due to the parallel
computation and sampling method.
