  We study global optimization (GOP) in the framework of non-linear inverse
problems with a unique solution. These problems are in general ill-posed.
Evaluation of the objective function is often expensive, as it implies the
solution of a non-trivial forward problem. The ill-posedness of these problems
calls for regularization while the high evaluation cost of the objective
function can be addressed with response surface techniques. The global
optimization using Radial Basis Function (RBF) as presented by Gutmann (2001)
is a response surface global optimization technique with regularizing aspects.
Alternatively, several publications put forward global optimization using a
probabilistic approach based upon Kriging as an efficient technique for
non-linear multi modal objective functions, thereby providing a credible
stopping rule (Jones2001). After comparing both concepts, we argue that in case
of non-linear inverse problems an adaptation of the RBF algorithm seems to be
the most promising approach.
