 Such randomness can also make the method less sensitive to modeling errors.Further, the injected randomness may enable the method to escape a local optimum and eventually to approach a global optimum.Indeed, this randomization principle is known to be a simple and effective way to obtain algorithms with "almost certain" good performance uniformly across many data sets, for many sorts of problems.