The GA can be run with a pool or generational population. When running the MLaGA with a generational population, a ML model is trained and utilized to search for a full generation of e.g. 150 candidates. When combining the MLaGA with the generational nested GA, a greater number of candidates are generated in total, compared with the traditional GA. However, the majority of candidates generated in the nested GA routine are discarded prior to the expensive energy evaluation step. Therefore, the MLaGA with a nested search, is able to locate the full convex hull of minima in an average of 1200 candidates. It is possible to reduce the total number of energy calculations through the employ of different acceptance criteria. Tournament acceptance was particularly efficient at reducing the number of required energy calculations, reducing to fewer than 600 for the search.