The authors draw from traditional genetic algorithms (NEAT http://nn.cs.utexas.edu/downloads/papers/stanley.ec02.pdf http://nn.cs.utexas.edu/downloads/papers/st

The authors draw from traditional genetic algorithms (NEAT http://nn.cs.utexas.edu/downloads/papers/stanley.ec02.pdf http://nn.cs.utexas.edu/downloads/papers/stanley.ec02.pdf ) but this appears to be one of the first papers (at least recently) where proposed architectures are not trained but rather given random weights. The authors here try to qualitatively distill the role of architecture (vs optimization) in neural net research. The work here is in a similar vein as the Lottery Ticket Hypothesis ( https://arxiv.org/pdf/1803.03635.pdf https://arxiv.org/pdf/1803.03635.pdf ), which found that deep nets (for vision) contain discriminative sub-networks at initialization time (due to random initialization), before training ever starts. While the authors of this work on architecture search say they hope to inspire the discovery of new architectures, a more immediately striking result of their work is that they get functioning systems from doing something “stupid” (i.e. not optimizing weights).