In population-based algorithms, center-based sampling theory has been proposed to focus the search in a promising region of the search space instead of searching less promising ones. It can be utilized during population initialization and/or generating successive generations.

My question in this regard, is there any counter-theory of sampling, which argue that the boundary region of the search space is also promising, because it is highly probable that the optimal solution lies in the boundary.