The crossover and mutation operators mix and alter the tentative solutions that were among the best individuals in the previous generation. While the solutions are expected to improve along a GA run on average, there is no guarantee that the best fitness at a certain generation is lower than its previous counterpart, and genes can drastically change in case of genetic drift, escaping from a region where the GM actually sits. A way to counteract this is the application of an elitism operator which consists in replacing a fraction f of the worst solutions by the best individuals of the previous generation. This makes sure that the best fragments of information found so far are automatically transferred to the offspring generation, which thus always contains the overall best solution. Such a selective pressure can improve the convergence speed, though the efficiency of any GA is dictated by its ability to balance between exploration and exploitation and elitism introduces the risk of losing diversity and converging prematurely to less-fit LM. 3 Computational details