  We apply the optimization algorithm Adaptive Simulated Annealing (ASA) to the
problem of analyzing data on a large population and selecting the best model to
predict that an individual with various traits will have a particular disease.
We compare ASA with traditional forward and backward regression on computer
simulated data. We find that the traditional methods of modeling are better for
smaller data sets whereas a numerically stable ASA seems to perform better on
larger and more complicated data sets.
