The control problem of soft-landing a toy lunar module sim-ulation is investigated in the context of neural nets. While traditional supervised back-propagation training is inappropriate for lack of train-ing exemplars, genetic algorithms allow a controller to be evolved with-out diiculty: Evolution is a form of unsupervised learning. A novelty introduced in this paper is the presentation of additional renormalized inputs to the net; experiments indicate that the presence of such inputs allows precision of control to be attained faster, when learning time is measured by the number of generations for which the GA must run to attain a certain mean performance.