  We present FitSKIRT, a method to efficiently fit radiative transfer models to
UV/optical images of dusty galaxies. These images have the advantage that they
have better spatial resolution compared to FIR/submm data. FitSKIRT uses the
GAlib genetic algorithm library to optimize the output of the SKIRT Monte Carlo
radiative transfer code. Genetic algorithms prove to be a valuable tool in
handling the multi- dimensional search space as well as the noise induced by
the random nature of the Monte Carlo radiative transfer code. FitSKIRT is
tested on artificial images of a simulated edge-on spiral galaxy, where we
gradually increase the number of fitted parameters. We find that we can recover
all model parameters, even if all 11 model parameters are left unconstrained.
Finally, we apply the FitSKIRT code to a V-band image of the edge-on spiral
galaxy NGC4013. This galaxy has been modeled previously by other authors using
different combinations of radiative transfer codes and optimization methods.
Given the different models and techniques and the complexity and degeneracies
in the parameter space, we find reasonable agreement between the different
models. We conclude that the FitSKIRT method allows comparison between
different models and geometries in a quantitative manner and minimizes the need
of human intervention and biasing. The high level of automation makes it an
ideal tool to use on larger sets of observed data.
