Genetic algorithms for the analysis of Bayesian hierarchical partition models

Hierarchical partition models (see Malec and Sedransk, 1992, Consonni and Veronese, 1995) aim at finding an optimal grouping (partition) of a set of experiments regarding a target variable. In this class of models the partition is regarded as an unknown parameter, and one of the main goals is computing the posterior distribution over the class of the possible partitions. This problem has been addressed in Sampietro and Veronese (1998), where a Metropolis-Hastings algorithm is applied. In this paper the performance of an alternative procedure, based on the logic of genetic algorithms, is evaluated. The results of the two approaches are compared, even if a conjoint use of them is to be advised.