  Neural networks have proven effective at solving difficult problems but
designing their architectures can be challenging, even for image classification
problems alone. Our goal is to minimize human participation, so we employ
evolutionary algorithms to discover such networks automatically. Despite
significant computational requirements, we show that it is now possible to
evolve models with accuracies within the range of those published in the last
year. Specifically, we employ simple evolutionary techniques at unprecedented
scales to discover models for the CIFAR-10 and CIFAR-100 datasets, starting
from trivial initial conditions and reaching accuracies of 94.6% (95.6% for
ensemble) and 77.0%, respectively. To do this, we use novel and intuitive
mutation operators that navigate large search spaces; we stress that no human
participation is required once evolution starts and that the output is a
fully-trained model. Throughout this work, we place special emphasis on the
repeatability of results, the variability in the outcomes and the computational
requirements.
