Global Optimization methods for Gravitational Lens Systems with
  Regularized Sources
  Several approaches exist to model gravitational lens systems. In this study,
we apply global optimization methods to find the optimal set of lens parameters
using a genetic algorithm. We treat the full optimization procedure as a
two-step process: an analytical description of the source plane intensity
distribution is used to find an initial approximation to the optimal lens
parameters. The second stage of the optimization uses a pixelated source plane
with the semilinear method to determine an optimal source. Regularization is
handled by means of an iterative method and the generalized cross validation
(GCV) and unbiased predictive risk estimator (UPRE) functions that are commonly
used in standard image deconvolution problems. This approach simultaneously
estimates the optimal regularization parameter and the number of degrees of
freedom in the source. Using the GCV and UPRE functions we are able to justify
an estimation of the number of source degrees of freedom found in previous
work. We test our approach by applying our code to a subset of the lens systems
included in the SLACS survey.
