  Computational models are of increasing complexity and their behavior may in
particular emerge from the interaction of different parts. Studying such models
becomes then more and more difficult and there is a need for methods and tools
supporting this process. Multi-objective evolutionary algorithms generate a set
of trade-off solutions instead of a single optimal solution. The availability
of a set of solutions that have the specificity to be optimal relative to
carefully chosen objectives allows to perform data mining in order to better
understand model features and regularities. We review the corresponding work,
propose a unifying framework, and highlight its potential use. Typical
questions that such a methodology allows to address are the following: what are
the most critical parameters of the model? What are the relations between the
parameters and the objectives? What are the typical behaviors of the model? Two
examples are provided to illustrate the capabilities of the methodology. The
features of a flapping-wing robot are thus evaluated to find out its
speed-energy relation, together with the criticality of its parameters. A
neurocomputational model of the Basal Ganglia brain nuclei is then considered
and its most salient features according to this methodology are presented and
discussed.
