In this paper, we describe some evolutionaryapproaches based on genetic algorithms to face the statisticalmodel selection problem using completely data-drivenalgorithms. As first, we propose an approach to selectmultivariate linear regression models as well as to buildARMA time series models. As second, we introduce amethodology to tackle the clustering problem in a modelbasedframework. We report the results from severalapplications and from simulated datasets and we compare theevolutionary approaches with some classical ones.
Evolutionary Approaches for Statistical Modelling / Minerva, Tommaso; Paterlini, Sandra. - STAMPA. - 2:(2002), pp. 2023-2028. (Intervento presentato al convegno WCCI 2002,Proc. of the Fourth Congress on Evolutionary Computation tenutosi a Honolulu, Haway nel May 2002) [10.1109/CEC.2002.1004554].
Evolutionary Approaches for Statistical Modelling
MINERVA, Tommaso;PATERLINI, Sandra
2002
Abstract
In this paper, we describe some evolutionaryapproaches based on genetic algorithms to face the statisticalmodel selection problem using completely data-drivenalgorithms. As first, we propose an approach to selectmultivariate linear regression models as well as to buildARMA time series models. As second, we introduce amethodology to tackle the clustering problem in a modelbasedframework. We report the results from severalapplications and from simulated datasets and we compare theevolutionary approaches with some classical ones.
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