Model selection - the process of selecting the best subset of predictor variables to define a model - is made in part fast and easy by today's computer power, which is cheap in terms of crunch time. Running a million regression models with eighteen candidate predictor variables (2 to the 18th power) takes only minutes. However, deciding the best subset among the million, or just collecting a dozen good models is a tedious task. The traditional approach to model selection uses significance testing, which is a rote process dulled by the lack of a creative component of constructing new variables from the original ones. The purpose of this article is to introduce a new method that automatically and simultaneously selects important original variables, and constructs new important variables from the original variables by finding patterns within the data (data mining), and lastly selects a model based on the best subset of original and constructed variables. The method is based on the assumption-free, nonparametric genetic paradigm inspired by Natural Selection - Darwin's Principle of Survival of the Fittest and the biological operations of reproduction, sexual recombination and mutation. The new method offers a clear advantage over current statistical methods, whose performance is dependent upon significance tests along with theoretical assumptions, predefined model formulations, and data-type restrictions. A case study is presented to illustrate the potential of the new method for building database marketing and CRM models with the GenIQ software implementation of the new method.