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Probabilistic grammar-based deep neuroevolution

Published: 13 July 2019 Publication History

Abstract

Designing deep neural networks by human engineers can be challenging because there are various choices of deep neural network structures. We developed a deep neuroevolution system to automatically design deep neural network structures using deep neuroevolution. Our approach defines a set of structures using a probabilistic grammar and searches for best network structures using Probabilistic Model Building Genetic Programming. Our approach takes advantage of the probabilistic dependencies found among the structures of networks. The system was applied to tackle the problem of the physiological signal classification of abnormal heart rhythm. In the classification problem, our discovered model is more accurate than AlexNet. Our discovered model uses about 2% of the total amount of parameters of AlexNet.

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Cited By

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  • (2023)Semi-Supervised Learning with Coevolutionary Generative Adversarial NetworksProceedings of the Genetic and Evolutionary Computation Conference10.1145/3583131.3590426(568-576)Online publication date: 15-Jul-2023
  • (2023)Semi-supervised generative adversarial networks with spatial coevolution for enhanced image generation and classificationApplied Soft Computing10.1016/j.asoc.2023.110890148(110890)Online publication date: Nov-2023
  • (2022)Coevolutionary generative adversarial networks for medical image augumentation at scaleProceedings of the Genetic and Evolutionary Computation Conference10.1145/3512290.3528742(367-376)Online publication date: 8-Jul-2022

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cover image ACM Conferences
GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion
July 2019
2161 pages
ISBN:9781450367486
DOI:10.1145/3319619
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

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Publication History

Published: 13 July 2019

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Author Tags

  1. deep neural network
  2. estimation of distribution programming

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GECCO '19
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GECCO '19: Genetic and Evolutionary Computation Conference
July 13 - 17, 2019
Prague, Czech Republic

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

View all
  • (2023)Semi-Supervised Learning with Coevolutionary Generative Adversarial NetworksProceedings of the Genetic and Evolutionary Computation Conference10.1145/3583131.3590426(568-576)Online publication date: 15-Jul-2023
  • (2023)Semi-supervised generative adversarial networks with spatial coevolution for enhanced image generation and classificationApplied Soft Computing10.1016/j.asoc.2023.110890148(110890)Online publication date: Nov-2023
  • (2022)Coevolutionary generative adversarial networks for medical image augumentation at scaleProceedings of the Genetic and Evolutionary Computation Conference10.1145/3512290.3528742(367-376)Online publication date: 8-Jul-2022

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