  We present three evolutionary symbolic regression-based classification
algorithms for binary and multinomial datasets: GPLearnClf, CartesianClf, and
ClaSyCo. Tested over 162 datasets and compared to three state-of-the-art
machine learning algorithms -- XGBoost, LightGBM, and a deep neural network --
we find our algorithms to be competitive. Further, we demonstrate how to find
the best method for one's dataset automatically, through the use of a
state-of-the-art hyperparameter optimizer.
