Artificial evolution plays an important role in several robotics projects. Most commonly, an evolutionary algorithm (EA) is used as a heuristic optimiser to solve some engineering problem, for instance an EA is used to find good robot controller. In these applications the human designers/experimenters orchestrate and manage the whole evolutionary problem solving process and incorporate the end result –that is, the (near-)optimal solution evolved by the EA– into the system as part of the deployment. During the operational period of the system the EA does not play any further role. In other words, the use of evolution is restricted to the pre-deployment stage. Another, more challenging type of application of evolution is where it serves as the engine behind adaptation during (rather than before) the operational period, without human intervention. In this section we elaborate on possible evolutionary approaches to this kind of applications, position these on a general feature map and test some of these set-ups experimentally to assess their feasibility.