Survey on Evolutionary Deep Learning: Principles, Algorithms, Applications and Open Issues
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by
Nan Li, Lianbo Ma, Guo Yu, Bing Xue, Mengjie Zhang, Yaochu Jin
2022
Abstract
Over recent years, there has been a rapid development of deep learning (DL)
in both industry and academia fields. However, finding the optimal
hyperparameters of a DL model often needs high computational cost and human
expertise. To mitigate the above issue, evolutionary computation (EC) as a
powerful heuristic search approach has shown significant merits in the
automated design of DL models, so-called evolutionary deep learning (EDL). This
paper aims to analyze EDL from the perspective of automated machine learning
(AutoML). Specifically, we firstly illuminate EDL from machine learning and EC
and regard EDL as an optimization problem. According to the DL pipeline, we
systematically introduce EDL methods ranging from feature engineering, model
generation, to model deployment with a new taxonomy (i.e., what and how to
evolve/optimize), and focus on the discussions of solution representation and
search paradigm in handling the optimization problem by EC. Finally, key
applications, open issues and potentially promising lines of future research
are suggested. This survey has reviewed recent developments of EDL and offers
insightful guidelines for the development of EDL.
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