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Surrogate-Assisted Evolutionary Deep Learning Using an End-to-End Random Forest-based Performance Predictor

Sun, Yanan, Wang, Handing, Xue, Bing, Jin, Yaochu, Yen, Gary G. and Zhang, Mengjie (2019) Surrogate-Assisted Evolutionary Deep Learning Using an End-to-End Random Forest-based Performance Predictor IEEE Transactions on Evolutionary Computation.

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Convolutional neural networks (CNNs) have shown remarkable performance in various real-world applications. Unfortunately, the promising performance of CNNs can be achieved only when their architectures are optimally constructed. The architectures of state-of-the-art CNNs are typically hand-crafted with extensive expertise in both CNNs and the investigated data, which consequently hampers the widespread adoption of CNNs for less experienced users. Evolutionary deep learning (EDL) is able to automatically design the best CNN architectures without much expertise. However, existing EDL algorithms generally evaluate the fitness of a new architecture by training from scratch, resulting in the prohibitive computational cost even operated on high-performance computers. In this paper, an endto- end offline performance predictor based on the random forest is proposed to accelerate the fitness evaluation in EDL. The proposed performance predictor shows promising performance in term of the classification accuracy and the consumed computational resources when compared with 18 state-of-the-art peer competitors by integrating it into an existing EDL algorithm as a case study. The proposed performance predictor is also compared with the other two representatives of existing performance predictors. The experimental results show the proposed performance predictor not only significantly speeds up the fitness evaluations, but also achieves the best prediction among the peer performance predictors.

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Computing Science
Authors :
Sun, Yanan
Xue, Bing
Yen, Gary G.
Zhang, Mengjie
Date : 2019
Copyright Disclaimer : © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Uncontrolled Keywords : Evolutionary deep learning; Performance predictor; Surrogate model; Random forest; Convolutional neural network
Related URLs :
Depositing User : Clive Harris
Date Deposited : 17 Jun 2019 07:35
Last Modified : 17 Jun 2019 07:35

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