Evolutionary neural networks for deep learning: a review

Evolutionary neural networks (ENNs) are an adaptive approach that combines the adaptive mechanism of Evolutionary algorithms (EAs) with the learning mechanism of Artificial Neural Network (ANNs). In view of the difficulties in design and development of DNNs, ENNs can optimize and supplement deep learning algorithm, and the more powerful neural network systems are hopefully built. Many valuable conclusions and results have been obtained in this field, especially in the construction of automated deep learning systems. This study conducted a systematic review of the literature on ENNs by using the PRISMA protocol. In literature analysis, the basic principles and development background of ENNs are firstly introduced. Secondly, the main research techniques are introduced in terms of connection weights, architecture design and learning rules, and the existing research results are summarized and the advantages and disadvantages of different research methods are analyzed. Then, the key technologies and related research progress of ENNs are summarized. Finally, the applications of ENNs are summarized and the direction of future work is proposed.