This thesis is about direct selection for evolvability in artificial evolutionary systems. The origin of evolvability-the capacity for adaptive evolution-is of great interest to evolutionary biologists, who have proposed many indirect selection mechanisms. In evolutionary computation and artificial life, these indirect selection mechanisms have been co-opted in order to engineer the evolution of evolvability into artificial evolution simulations. Very little work has been done on direct selection, and so this thesis investigates the extent to which we should select for evolvability. I show in a simple theoretical model the existence of conditions in which selection for a weighted sum of fitness and evolvability achieves greater long-term fitness than selection for fitness alone. There are no conditions, within the model, in which it is beneficial to select more for evolvability than for fitness. Subsequent empirical work compares episodic group selection for evolvability (EGS)-an algorithm that selects for evolvability estimates calculated from noisy samples-with an algorithm that selects for fitness alone on four fitness functions taken from the literature. The long-term fitness achieved by EGS does not exceed that of selection for fitness alone in any region of the parameter space. However, there are regions of the parameter space in which EGS achieves greater long-term evolvability. A modification of the algorithm, EGS-AR, which incorporates a recent best-arm identification algorithm, reliably outperforms EGS across the parameter space, in terms of both eventual fitness and eventual evolvability. The thesis concludes that selection for estimated evolvability may be a viable strategy for solving time-varying problems.