  The effort devoted to hand-crafting neural network image classifiers has
motivated the use of architecture search to discover them automatically.
Although evolutionary algorithms have been repeatedly applied to neural network
topologies, the image classifiers thus discovered have remained inferior to
human-crafted ones. Here, we evolve an image classifier---AmoebaNet-A---that
surpasses hand-designs for the first time. To do this, we modify the tournament
selection evolutionary algorithm by introducing an age property to favor the
younger genotypes. Matching size, AmoebaNet-A has comparable accuracy to
current state-of-the-art ImageNet models discovered with more complex
architecture-search methods. Scaled to larger size, AmoebaNet-A sets a new
state-of-the-art 83.9% / 96.6% top-5 ImageNet accuracy. In a controlled
comparison against a well known reinforcement learning algorithm, we give
evidence that evolution can obtain results faster with the same hardware,
especially at the earlier stages of the search. This is relevant when fewer
compute resources are available. Evolution is, thus, a simple method to
effectively discover high-quality architectures.
