  Optimization of expensive computer models with the help of Gaussian process
emulators in now commonplace. However, when several (competing) objectives are
considered, choosing an appropriate sampling strategy remains an open question.
We present here a new algorithm based on stepwise uncertainty reduction
principles to address this issue. Optimization is seen as a sequential
reduction of the volume of the excursion sets below the current best solutions,
and our sampling strategy chooses the points that give the highest expected
reduction. Closed-form formulae are provided to compute the sampling criterion,
avoiding the use of cumbersome simulations. We test our method on numerical
examples, showing that it provides an efficient trade-off between exploration
and intensification.
