  Character-level convolutional neural networks (char-CNN) require no knowledge
of the semantic or syntactic structure of the language they classify. This
property simplifies its implementation but reduces its classification accuracy.
Increasing the depth of char-CNN architectures does not result in breakthrough
accuracy improvements. Research has not established which char-CNN
architectures are optimal for text classification tasks. Manually designing and
training char-CNNs is an iterative and time-consuming process that requires
expert domain knowledge. Evolutionary deep learning (EDL) techniques, including
surrogate-based versions, have demonstrated success in automatically searching
for performant CNN architectures for image analysis tasks. Researchers have not
applied EDL techniques to search the architecture space of char-CNNs for text
classification tasks. This article demonstrates the first work in evolving
char-CNN architectures using a novel EDL algorithm based on genetic
programming, an indirect encoding and surrogate models, to search for
performant char-CNN architectures automatically. The algorithm is evaluated on
eight text classification datasets and benchmarked against five manually
designed CNN architecture and one long short-term memory (LSTM) architecture.
Experiment results indicate that the algorithm can evolve architectures that
outperform the LSTM in terms of classification accuracy and five of the
manually designed CNN architectures in terms of classification accuracy and
parameter count.
