Here we report first results of applying Genetic Programming to obtain equations for constructing reduced-order models of quantum systems, with a particular interest in nuclear Density Functional Theory. We employ the reduced basis method to obtain reduced coordinates as the amplitudes of the reduced basis, and use genetic programming to avoid the need of constructing the reduced equations through, for example, a Galerkin projection. The resulting reduced-order models show excellent performance in both accuracy and speed, including for extrapolations in the control parameters, and show promise as an effective method for emulating computationally demanding calculations in Nuclear Physics
