Channel pruning has made major headway in the design of efficient deep learning models. Conventional approaches adopt human-made pruning functions to score channels' importance for channel pruning, which requires domain knowledge and could be sub-optimal. In this work, we propose an end-to-end framework to automatically discover strong pruning metrics. Specifically, we craft a novel design space for expressing pruning functions and leverage an evolution strategy, genetic programming, to evolve high-quality and tra…