Guided Evolution with Binary Discriminators for ML Program Search
John D. Co-Reyes Yingjie Miao George Tucker Aleksandra Faust Esteban Real
How to automatically design better machine learning programs is an open
problem within AutoML. While evolution has been a popular tool to search for
better ML programs, using learning itself to guide the search has been less
successful and less understood on harder problems but has the promise to
dramatically increase the speed and final performance of the optimization
process. We propose guiding evolution with a binary discriminator, trained
online to distinguish which program is better given a pair of programs. The
discriminator selects better programs without having to perform a costly
evaluation and thus speed up the convergence of evolution. Our method can
encode a wide variety of ML components including symbolic optimizers, neural
architectures, RL loss functions, and symbolic regression equations with the
same directed acyclic graph representation. By combining this representation
with modern GNNs and an adaptive mutation strategy, we demonstrate our method
can speed up evolution across a set of diverse problems including a 3.7x
speedup on the symbolic search for ML optimizers and a 4x speedup for RL loss
functions.