  Rendering to their abilities to provide ubiquitous connectivity, flexibly and
cost effectively, unmanned aerial vehicles (UAVs) have been getting more and
more research attention. To take the UAVs' performance to the next level,
however, they need to be merged with some other technologies like
non-orthogonal multiple access (NOMA) and millimeter wave (mmWave), which both
promise high spectral efficiency (SE). As managing UAVs efficiently may not be
possible using model-based techniques, another key innovative technology that
UAVs will inevitably need to leverage is artificial intelligence (AI).
Designing an AI-based technique that adaptively allocates radio resources and
places UAVs in 3D space to meet certain communication objectives, however, is a
tough row to hoe. In this paper, we propose a neuroevolution of augmenting
topologies NEAT framework, referred to as ProSky, to manage NOMA-mmWave-UAV
networks. ProSky exhibits a remarkable performance improvement over a
model-based method. Moreover, ProSky learns 5.3 times faster than and
outperforms, in both SE and energy efficiency EE while being reasonably fair, a
deep reinforcement learning DRL based scheme. The ProSky source code is
accessible to use here: https://github.com/Fouzibenfaid/ProSky
