  Na\"ive restarts of global optimization solvers when operating on multimodal
search landscapes may resemble the Coupon's Collector Problem, with a potential
to waste significant function evaluations budget on revisiting the same basins
of attractions. In this paper, we assess the degree to which such ``duplicate
restarts'' occur on standard multimodal benchmark functions, which defines the
\textit{redundancy potential} of each particular landscape. We then propose a
repelling mechanism to avoid such wasted restarts with the CMA-ES and
investigate its efficacy on test cases with high redundancy potential compared
to the standard restart mechanism.
