  Neural architecture search (NAS) is a hot topic in the field of automated
machine learning and outperforms humans in designing neural architectures on
quite a few 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 CGPNAS, to solve sentence
classification task. To evolve the architectures under the framework of CGP,
the operations such as convolution are identified as the types of function
nodes of CGP, and the evolutionary operations are designed based on
Evolutionary Strategy. The experimental results show that the searched
architectures are comparable with the performance of human-designed
architectures. We verify the ability of domain transfer of our evolved
architectures. The transfer experimental results show that the accuracy
deterioration is lower than 2-5%. Finally, the ablation study identifies the
Attention function as the single key function node and the linear
transformations along could keep the accuracy similar with the full evolved
architectures, which is worthy of investigation in the future.

Neural and Evolutionary Computing