GAz: A Genetic Algorithm for Photometric Redshift Estimation
  We present a new approach to the problem of estimating the redshift of
galaxies from photometric data. The approach uses a genetic algorithm combined
with non-linear regression to model the 2SLAQ LRG data set with SDSS DR7
photometry. The genetic algorithm explores the very large space of high order
polynomials while only requiring optimisation of a small number of terms. We
find a $\sigma_{\text{rms}}=0.0408\pm 0.0006$ for redshifts in the range
$0.4<z< 0.7$. These results are competitive with the current state-of-the-art
but can be presented simply as a polynomial which does not require the user to
run any code. We demonstrate that the method generalises well to other data
sets and redshift ranges by testing it on SDSS DR11 and on simulated data. For
other datasets or applications the code has been made available at
https://github.com/rbrthogan/GAz.
