Evolutionary approaches for statistical modelling

In this paper, we describe some evolutionary approaches based on genetic algorithms to deal with the statistical model selection problem using completely data-driven algorithms. First, we propose an approach to select multivariate linear regression models as well as to build ARMA time-series models. Then we introduce a methodology to tackle the clustering problem in a model-based framework. We report the results from several applications and from simulated data sets, and we compare the evolutionary approaches with some classical ones.