  The diagnostic performance of most of the deep learning models is greatly
affected by the selection of model architecture and hyperparameters. Manual
selection of model architecture is not feasible as training and evaluating the
different architectures of deep learning models is a time-consuming process.
Therefore, we have proposed a novel framework of evolutionary deep neural
network which uses policy gradient to guide the evolution of DNN architecture
towards maximum diagnostic accuracy. We have formulated a policy gradient-based
controller which generates an action to sample the new model architecture at
every generation such that the optimality is obtained quickly. The fitness of
the best model obtained is used as a reward to update the policy parameters.
Also, the best model obtained is transferred to the next generation for quick
model evaluation in the NSGA-II evolutionary framework. Thus, the algorithm
gets the benefits of fast non-dominated sorting as well as quick model
evaluation. The effectiveness of the proposed framework has been validated on
three datasets: the Air Compressor dataset, Case Western Reserve University
dataset, and Paderborn university dataset.
