  Neural architecture search (NAS) is a hot topic in the field of AutoML, and
has begun to outperform human-designed architectures on many machine learning
tasks. Motivated by the natural representation form of neural networks by the
Cartesian genetic programming (CGP), we propose an evolutionary approach of NAS
based on CGP, called CPGNAS, for CNN architectures solving sentence
classification task. To evolve the CNN architectures under the framework of
CGP, the existing key operations are identified as the types of function nodes
of CGP and the evolutionary operations are designed based on evolutionary
strategy (ES). The experimental results show that the searched architecture can
reach the accuracy of human-designed architectures. The ablation tests identify
the Attention function as the single key function node and the Convolution and
Attention as the joint key function nodes. However, the linear transformations
along could keep the accuracy of evolved architectures over 70%, which is worth
of investigating in the future.
