  The computational burden of running a complex computer model can make
optimization impractical. Gaussian Processes (GPs) are statistical surrogates
(also known as emulators) that alleviate this issue since they cheaply replace
the computer model. As a result, the exploration vs. exploitation trade-off
strategy can be accelerated by building a GP surrogate. In this paper, we
propose a new surrogate-based optimization scheme that minimizes the number of
evaluations of the computationally expensive function. Taking advantage of
parallelism of the evaluation of the unknown function, the uncertain regions
are explored simultaneously, and a batch of input points is chosen using Mutual
Information for Computer Experiments (MICE), a sequential design algorithm
which maximizes the information theoretic Mutual Information over the input
space. The computational efficiency of interweaving the optimization scheme
with MICE (optim-MICE) is examined and demonstrated on test functions.
Optim-MICE is compared with state-of-the-art heuristics such as Efficient
Global Optimization (EGO) and GP-Upper Confidence Bound (GP-UCB). We
demonstrate that optim-MICE outperforms these schemes on a large range of
computational experiments.
