  The main problems in modeling interacting galaxies are the extended parameter
space and the fairly high CPU costs of self-consistent N-body simulations.
Therefore, traditional modeling techniques suffer from either extreme CPU
demands or trapping in local optima (or both). A very promising alternative
approach are evolutionary algorithms which mimic natural adaptation in order to
optimize the numerical models. One main advantage is their very weak dependence
on starting points which makes them much less prone to trapping in local
optima. We present a Genetic Algorithm (GA) coupled with a fast (but not
self-consistent) restricted N-body solver. This combination allows us to
identify interesting regions of parameter space within only a few CPU hours on
a standard PC or a few CPU minutes on a parallel computer. Especially, we
demonstrate here the ability of GA-based fitting procedures to analyse
observational data automatically, provided the data are sufficiently accurate.
