Blurb:: Total batch size in parallel EGO

Description:: Refinement candidates are generated by an acquisition
function such as maximum expected improvement, which balances
exploration and exploitation.  Refinement candidates can also be
generated by purely explorative metrics such as maximum prediction
variance.  For a specified \c batch_size, \c exploration specifies the
subset of this total that will be dedicated to pure exploration of the
parameter space.

<b> Default Behavior </b>
All of the batch size is devoted to the standard acquisition approach,
balancing exploration and exploitation.

Topics::

Examples::
\verbatim
method,
        efficient_global
      seed = 1237
      batch_size = 8    # total
      exploration = 2   # 2 out of 8
\endverbatim

Theory::
Faq::
See_Also::
