  Evolutionary Neural Architecture Search (ENAS) can automatically design the
architectures of Deep Neural Networks (DNNs) using evolutionary computation
algorithms. However, most ENAS algorithms require intensive computational
resource, which is not necessarily available to the users interested.
Performance predictors are a type of regression models which can assist to
accomplish the search, while without exerting much computational resource.
Despite various performance predictors have been designed, they employ the same
training protocol to build the regression models: 1) sampling a set of DNNs
with performance as the training dataset, 2) training the model with the mean
square error criterion, and 3) predicting the performance of DNNs newly
generated during the ENAS. In this paper, we point out that the three steps
constituting the training protocol are not well though-out through intuitive
and illustrative examples. Furthermore, we propose a new training protocol to
address these issues, consisting of designing a pairwise ranking indicator to
construct the training target, proposing to use the logistic regression to fit
the training samples, and developing a differential method to building the
training instances. To verify the effectiveness of the proposed training
protocol, four widely used regression models in the field of machine learning
have been chosen to perform the comparisons on two benchmark datasets. The
experimental results of all the comparisons demonstrate that the proposed
training protocol can significantly improve the performance prediction accuracy
against the traditional training protocols.
