  For a class of stochastic restart algorithms we address the effect of a
nonzero level of randomization in maximizing the convergence rate for general
energy landscapes. The resulting characterization of the optimal level of
randomization is investigated computationally for random as well as parametric
families of rugged energy landscapes.
