Survey on Evolutionary Recurrent Neural Networks

Abstract

Evolutionary computation utilizes natural selection mechanisms and genetic laws in the process of biological evolution to solve optimization problems.The accuracy and efficiency of the evolutionary recurrent neural network model depends on the optimization effect of parameters and the structures.The utilization of evolutionary computation to solve the problem of adaptive optimization of parameters and structures in recurrent neural networks is a hot spot of automated deep learning.This paper summarizes the algorithms that combine evolutionary algorithms and recurrent neural networks.Firstly,it briefly reviews the traditional categories,common algorithms,and advantages of evolutionary computation.Next,it briefly introduces the structures and characteristics of the recurrent neural network models and analyzes the influencing factors of recurrent neural network perfor-mance.Then,it analyzes the algorithmic framework of evolutionary recurrent neural networks,and the current research development of evolutionary recurrent neural networks from weight optimization,hyperparameter optimization and structure optimization.Besides,other work on evolutionary recurrent neural networks is analyzed.Finally,it points out the challenges and the deve-lopment trend of evolutionary recurrent neural networks.

Keywords

• recurrent neural network|evolutionary computation|weight optimization|hyperparameter optimization|optimization of structure|ensemble learning|transfer learning