Symbolic Regression is investigated as a tool for identifying analytical expressions which provide an estimate of orbit transfer cost, evaluated in terms of required veloc- ity increment, as a function of initial and target orbit geometry. Different approaches are considered to identify the best approach to sample the problem parameter space and the algorithm which performs better, in the framework of Genetic Programming. Each resulting method is tested for five different orbit transfer geometries between coplanar circular and elliptical orbits. Results demonstrate the viability of the ap- proach, although when the number of problem parameter increases, computational cost becomes sizeable. Also, local minima may be filtered by the regression
