  A novel efficient method for computing the Knowledge-Gradient policy for
Continuous Parameters (KGCP) for deterministic optimization is derived. The
differences with Expected Improvement (EI), a popular choice for Bayesian
optimization of deterministic engineering simulations, are explored. Both
policies and the Upper Confidence Bound (UCB) policy are compared on a number
of benchmark functions including a problem from structural dynamics. It is
empirically shown that KGCP has similar performance as the EI policy for many
problems, but has better convergence properties for complex (multi-modal)
optimization problems as it emphasizes more on exploration when the model is
confident about the shape of optimal regions. In addition, the relationship
between Maximum Likelihood Estimation (MLE) and slice sampling for estimation
of the hyperparameters of the underlying models, and the complexity of the
problem at hand, is studied.
