Low-lying minima search and refinement GA optimization offers the additional advantage that one can profit from all the LM visited during evolution. Maximizing the number of low-energy structures is therefore important in order to capture all surrogate candidates likely to relax to the desired reference minimum. As an example, the progression of the number of new minima explored is shown in Figure for a single GA as well as after several executions. The average number of minima found over the GA iterations is very similar with or without elitism and reaches a plateau after a certain number of GA generations (Fig. ). Figure shows that it is more efficient to perform independent runs in parallel to improve the search and sample more LM, rather than extending a single execution with more generations. In this case, however, the use of elitism can alter diversity and reduce the exploration of low-lying LM, which is observed for all schemes (Fig. ).