  We apply a combination of a Genetic Algorithms (GA) and Support Vector
Machines (SVM) machine learning algorithm to solve two important problems faced
by the astronomical community: star/galaxy separation, and photometric redshift
estimation of galaxies in survey catalogs. We use the GA to select the relevant
features in the first step, followed by optimization of SVM parameters in the
second step to obtain an optimal set of parameters to classify or regress, in
process of which we avoid over-fitting. We apply our method to star/galaxy
separation in Pan-STARRS1 data. We show that our method correctly classifies
98% of objects down to i_P1= 24.5, with a completeness (or true positive rate)
of 99% for galaxies, and 88% for stars. By combining colors with morphology,
our star/classification method yields better results than the new SExtractor
classifier spread_model in particular at the faint end (i_P1>22). We also use
our method to derive photometric redshifts for galaxies in the COSMOS bright
multi-wavelength dataset down to an error in (1+z) of sigma=0.013, which
compares well with estimates from SED fitting on the same data (sigma=0.007)
while making a significantly smaller number of assumptions.
