In Evolutionary Robotics (ER), controllers are assessed in a single or a few environments. As a consequence, good performances in new di erent contexts are not guaranteed. While a lot of ER works deal with robustness, i.e. the ability to perform well on new contexts close to the ones used for evaluation, no current approach is able to promote broader generalisation abilities without any assumption on the new contexts. In this paper, we introduce the ProGAb approach, which is based on the standard three data sets methodology of supervised machine learning, and compare it to state-of- the-art ER methods on two simulated robotic tasks: a navi- gation task in a T-maze and a more complex ball-collecting task in an arena. In both applications, the ProGAb ap- proach: (1) produced controllers with better generalisation abilities than the other methods; (2) needed two to three times fewer evaluations to discover such solutions.