Simultaneous selection of variables and smoothing parameters by genetic algorithms

In additive models the problem of variable selection is strongly
linked to the choice of the amount of smoothing used for components
that represent metrical variables. Many software packages use
separate toolsto solve the different tasks of variable selection
and smoothing parameter choice. The combinationof these tools often
leads to inappropriate results. In this paper we propose a
simultaneous choice of variables and smoothing parameters based on
genetic algorithms. Common genetic algorithms have to be modified
since inclusion of variables and smoothing have to be coded
separately but are linked in the search for optimal solutions. The
basic tool for fitting the additive model is the penalized
expansion in B-splines.