  Neural Architecture Search has achieved state-of-the-art performance in a
variety of tasks, out-performing human-designed networks. However, many
assumptions, that require human definition, related with the problems being
solved or the models generated are still needed: final model architectures,
number of layers to be sampled, forced operations, small search spaces, which
ultimately contributes to having models with higher performances at the cost of
inducing bias into the system. In this paper, we propose HMCNAS, which is
composed of two novel components: i) a method that leverages information about
human-designed models to autonomously generate a complex search space, and ii)
an Evolutionary Algorithm with Bayesian Optimization that is capable of
generating competitive CNNs from scratch, without relying on human-defined
parameters or small search spaces. The experimental results show that the
proposed approach results in competitive architectures obtained in a very short
time. HMCNAS provides a step towards generalizing NAS, by providing a way to
create competitive models, without requiring any human knowledge about the
specific task.
