Symbolic regression is a method by which an equation of the system describing it is inferred from a set of points in space. Since explicit formulas have been the subject of research by this method over the years, this thesis decided to deal with the symbolic regression of implicit functions. Implicit functions are much more expressive due to their certain properties and are harder to infer than explicit ones. Therefore, different heuristics have been made to arrive at satisfactory solutions. A simpler method involved minimizing the standard deviation of evaluations for candidate solutions (equations) to the problem. By lowering the scattering for all the points in the set, the arbitrary algorithm was guided to the final solution of the regression. The second method used the comparison of partial derivations between solution equations and experimentally obtained derivatives. In two-dimensional systems, the derivation calculation is simple, while for multidimensional systems it is necessary to determine the plane in the local vicinity of some central point to obtain the correct derivation. The least squares method was used to determine the planes, and in the end it worked very well for a certain subset of functions which were conic or spherical, while more research and attempts are still needed on others to obtain more accurate data. Heuristics were driven by genetic programming algorithms and gene expression programming (GEP) which have shown extremely good results for a certain subset of functions.