Fitting Isochrones to Open Cluster photometric data: A new global
  optimization tool
  We present a new technique to fit color-magnitude diagrams of open clusters
based on the Cross-Entropy global optimization algorithm. The method uses
theoretical isochrones available in the literature and maximizes a weighted
likelihood function based on distances measured in the color-magnitude space.
The weights are obtained through a non parametric technique that takes into
account the star distance to the observed center of the cluster, observed
magnitude uncertainties, the stellar density profile of the cluster among
others. The parameters determined simultaneously are distance, reddening, age
and metallicity. The method takes binary fraction into account and uses a
Monte-Carlo approach to obtain uncertainties on the determined parameters for
the cluster by running the fitting algorithm many times with a re-sampled data
set through a bootstrapping procedure. We present results for 9 well studied
open clusters, based on 15 distinct data sets, and show that the results are
consistent with previous studies. The method is shown to be reliable and free
of the subjectivity of most previous visual isochrone fitting techniques.
