  Sequential model-based optimization (SMBO) approaches are algorithms for
solving problems that require computationally or otherwise expensive function
evaluations. The key design principle of SMBO is a substitution of the true
objective function by a surrogate, which is used to propose the point(s) to be
evaluated next.
  SMBO algorithms are intrinsically modular, leaving the user with many
important design choices. Significant research efforts go into understanding
which settings perform best for which type of problems. Most works, however,
focus on the choice of the model, the acquisition function, and the strategy
used to optimize the latter. The choice of the initial sampling strategy,
however, receives much less attention. Not surprisingly, quite diverging
recommendations can be found in the literature.
  We analyze in this work how the size and the distribution of the initial
sample influences the overall quality of the efficient global
optimization~(EGO) algorithm, a well-known SMBO approach. While, overall, small
initial budgets using Halton sampling seem preferable, we also observe that the
performance landscape is rather unstructured. We furthermore identify several
situations in which EGO performs unfavorably against random sampling. Both
observations indicate that an adaptive SMBO design could be beneficial, making
SMBO an interesting test-bed for automated algorithm design.
