  Several problems in signal processing are addressed by expert systems which
take into account a set of priors on the sought signals and systems. For
instance, blind source separation is often tackled by means of a mono-objective
formulation which relies on a separation criterion associated with a given
property of the sought signals (sources). However, in many practical
situations, there are more than one property to be exploited and, as a
consequence, a set of separation criteria may be used to recover the original
signals. In this context, this paper addresses the separation problem by means
of an approach based on multi-objective optimization. Differently from the
existing methods, which provide only one estimate for the original signals, our
proposal leads to a set of solutions that can be utilized by the system user to
take his/her decision. Results obtained through numerical experiments over a
set of biomedical signals highlight the viability of the proposed approach,
which provides estimations closer to the mean squared error solutions compared
to the ones achieved via a mono-objective formulation. Moreover, since our
proposal is quite general, this work also contributes to encourage future
researches to develop expert systems that exploit the multi-objective
formulation in different source separation problems.
