  NM-landscapes have been recently introduced as a class of tunable rugged
models. They are a subset of the general interaction models where all the
interactions are of order less or equal $M$. The Boltzmann distribution has
been extensively applied in single-objective evolutionary algorithms to
implement selection and study the theoretical properties of model-building
algorithms. In this paper we propose the combination of the multi-objective
NM-landscape model and the Boltzmann distribution to obtain Pareto-front
approximations. We investigate the joint effect of the parameters of the
NM-landscapes and the probabilistic factorizations in the shape of the Pareto
front approximations.
