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Neural architecture search (NAS) is a hot topic in the field of automatedmachine learning and outperforms humans in designing neural architectures onquite a few machine learning tasks. Motivated by the natural representationform of neural networks by the Cartesian genetic programming (CGP), we proposean evolutionary approach of NAS based on CGP, called CGPNAS, to solve sentenceclassification task. To evolve the architectures under the framework of CGP,the operations such as convolution are identified as the types of functionnodes of CGP, and the evolutionary operations are designed based onEvolutionary Strategy. The experimental results show that the searchedarchitectures are comparable with the performance of human-designedarchitectures. We verify the ability of domain transfer of our evolvedarchitectures. The transfer experimental results show that the accuracydeterioration is lower than 2-5%. Finally, the ablation study identifies theAttention function as the single key function node and the lineartransformations along could keep the accuracy similar with the full evolvedarchitectures, which is worthy of investigation in the future.

Neural Architecture Search based on Cartesian Genetic Programming Coding Method