We also introduce simple techniques to accelerate the architecture search: rich initialization and early network training termination. The CNN structure and connectivity, represented by the CGP, are optimized to maximize accuracy using the evolutionary algorithm. Our method uses Cartesian genetic programming (CGP) to encode the CNN architectures, adopting highly functional modules such as a convolutional block and tensor concatenation, as the node functions in CGP. In this article, we attempt to automatically construct high-performing CNN architectures for a given task. However, as the network architectures become deeper and more complex, designing CNN architectures requires more expert knowledge and trial and error. The convolutional neural network (CNN), one of the deep learning models, has demonstrated outstanding performance in a variety of computer vision tasks.
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