  The back-propagation algorithm is the cornerstone of deep learning. Despite
its importance, few variations of the algorithm have been attempted. This work
presents an approach to discover new variations of the back-propagation
equation. We use a domain specific lan- guage to describe update equations as a
list of primitive functions. An evolution-based method is used to discover new
propagation rules that maximize the generalization per- formance after a few
epochs of training. We find several update equations that can train faster with
short training times than standard back-propagation, and perform similar as
standard back-propagation at convergence.
