A Review of Deep Learning Models Based on Neuroevolution

doi:10.12263/DZXB.20200139

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ACTA ELECTRONICA SINICA ›› 2021, Vol. 49 ›› Issue (2) : 372-379. DOI: 10.12263/DZXB.20200139

SURVEYS AND REVIEWS

A Review of Deep Learning Models Based on Neuroevolution

HAN Chong, WANG Jun-li, WU Yu-xi, ZHANG Chao-bo

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Abstract

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.

Key words

neuroevolution / deep learning / evolutionary computation / convolutional neural networks / generative adversarial networks / autoencoder / long short-term memory networks / deep reinforcement learning

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HAN Chong, WANG Jun-li, WU Yu-xi, ZHANG Chao-bo. A Review of Deep Learning Models Based on Neuroevolution[J]. Acta Electronica Sinica, 2021, 49(2): 372-379. https://doi.org/10.12263/DZXB.20200139

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Funding

National Key Research and Development Program of China (No.2017YFA0700602); National Natural Science Foundation of China (No.61672381); Fundamental Research Funds for the Central Universities