Of course, these two approaches are often combined. The most accurate functionals (including meta-GGAs, hybrids, and RPA-like functionals) generalize Kohn-Sham theory 67 by employing as arguments of the energy density not only the electron density and its gradient, but also the occupied or even the unoccupied orbitals or one-electron wave functions, and by optimizing those arguments. A sometimes important but seldom discussed step in the development of a functional is "deconstruction": removing what is wrong or unnecessary, as in the transition from gradient expansions to generalized gradient expansions. 123 As suggested in contribution (3.1.2), in case of semiempirical functionals (see contribution (3.1.1)) which may involve dozens of parameters, it is especially important to remove all the fitted parameters with statistically insignificant weights in the fits. The reason is that these parameters introduce noise in the calculations and restrain severely the application domain to the classes of molecular systems that have been used in the training set. Approaches like variance analysis spring to mind, but more elaborate methods may also be used -for example, Mardirossian and Head-Gordon 236 have detailed the strategy they used for a combinatorial approach to handle the problem, which is made more complicated by the fact that the objective function to be optimized (usually a least-squares sum) is a (linear) combination of inhomogeneous quantities (energies, structural data, other physical properties) that are combined with ad hoc weights in the objective function.