With the development of deep learning

it has been competent to various types of tasks

such as identification

classification

and generation. However

the structures or parameters of artificial neural networks cannot be only a little changed for different task

experts need to adjust the structures or parameters of the neural network. In such situations

the method of automatically adjusting the structures or parameters of the artificial neural network has become a research hotspot

among these methods

neuroevolution inspired by Darwin's natural evolution theory has become the main optimization method for that. Deep learning models optimized by neuroevolution based on population

evolving through mutation

crossover and other operations

can automatically and gradually construct the neural network and then choose the most optimal deep learning model. This paper summarizes the neuroevolution and the evolutionary computation. It elaborates various deep learning models based on neuroevolution

and analyzes the performance of these models. It concludes prospects of the deep learning model based on neuroevolution and discusses the next research directions.