A library of catalysts was designed for asymmetric-hydrogen transfer to acetophenone. At first, the whole library was submitted to evaluation using high-throughput experiments (HTE). The catalysts were listed in ascending order, with respect to their performance, and best catalysts were identified. In the second step, various simulated evolution experiments, based on a genetic algorithm, were applied to this library. A small part of the library, called the mother generation (G0), thus evolved from generation to generation. The goal was to use our collection of HTE data to adjust the parameters of the genetic algorithm, in order to obtain a maximum of the best catalysts within a minimal number of generations. It was namely found that simulated evolution's results depended on the selection of G0 and that a random G0 should be preferred. We also demonstrated that it was possible to get 5 to 6 of the ten best catalysts while investigating only 10 % of the library. Moreover, we developed a double algorithm making this result still achievable if the evolution started with one of the worst G0.