Estimating photometric redshifts with genetic algorithms

Photometry is used as a cheap and easy way to estimate redshifts of galaxies, which would otherwise require considerable amounts of expensive telescope time. However, the analysis of photometric redshift datasets is a task where it is sometimes difficult to achieve a high classification accuracy. This work presents a custom Genetic Algorithm (GA) for mining the Hubble Deep Field North (HDF-N) datasets to achieve accurate IF-THEN classification rules. This kind of knowledge representation has the advantage of being intuitively comprehensible to the user, facilitating astronomers' interpretation of discovered knowledge. The GA is tested against the state of the art decision tree algorithm C5.0 [6] achieving significantly better results.