Guided Evolution with Binary Discriminators for ML Program Search
This addresses the challenge of slow and inefficient search in AutoML for researchers and practitioners, offering a novel approach that is incremental in improving existing evolutionary methods.
The paper tackles the problem of automatically designing better machine learning programs by proposing a method that guides evolution with a binary discriminator to speed up convergence, achieving a 3.7x speedup on symbolic search for ML optimizers and a 4x speedup for RL loss functions.
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.