RQK fitness functions for regression models 
In searching for regression models by evolutionary methods, optimising only the leaveoneout explained variance, Q2 has been demonstrated to be often overoptimistic and not able to give optimal predictive models. In fact, the final selected models often turned out not to be as predictive as expected if more severe validation was applied. On analysing these models, it was found that chance correlation and noisy variables are frequently the cause of their lack of predictivity.

RQK fitness functions have been recently proposed with the aim to highlight patologies present in regression models. These functions have been implemented in the software MOBYDIGS, for the variable subset selection using genetic algorithms. 