Genetic algorithms applied to feature selection in PLS regression: how and when to use them

Genetic algorithms (GA) are very useful in solving complex problems of optimization. The selection of the best subset of variables is surely one of them. In this paper, a new approach is proposed, and the positive and negative aspects of the application of GA in selecting variables for a partial least squares (PLS) model are taken into account. Finally, the analysis of the results obtained on several real data sets allows to find a rationale for a sensible application, showing that, if correctly applied, this technique almost always produces very good results.