Automated neural network construction with similarity sensitive evolutionary algorithms

Haiman Tian, Shu Ching Chen, Mei Ling Shyu, Stuart H. Rubin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Deep learning has been successfully applied to a wide variety of tasks. It generates reusable knowledge that allows transfer learning to significantly impact more scientific research areas. However, there is no automatic way to build a new model that guarantees an adequate performance. In this paper, we propose an automated neural network construction framework to overcome the limitations found in current approaches using transfer learning. Currently, researchers spend much time and effort to understand the characteristics of the data when designing a new network model. Therefore, the proposed method leverages the strength in evolutionary algorithms to automate the search and optimization process. Similarities between the individuals are also considered during the cycled evolutionary process to avoid sticking to a local optimal. Overall, the experimental results effectively reach optimal solutions proving that a time-consuming task could also be done by an automated process that exceeds the human ability to select the best hyperparameters.

Original languageEnglish (US)
Title of host publicationProceedings - 2019 IEEE 20th International Conference on Information Reuse and Integration for Data Science, IRI 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages283-290
Number of pages8
ISBN (Electronic)9781728113371
DOIs
StatePublished - Jul 2019
Event20th IEEE International Conference on Information Reuse and Integration for Data Science, IRI 2019 - Los Angeles, United States
Duration: Jul 30 2019Aug 1 2019

Publication series

NameProceedings - 2019 IEEE 20th International Conference on Information Reuse and Integration for Data Science, IRI 2019

Conference

Conference20th IEEE International Conference on Information Reuse and Integration for Data Science, IRI 2019
CountryUnited States
CityLos Angeles
Period7/30/198/1/19

Keywords

  • Automated neural network construction
  • Deep learning
  • Evolutionary algorithm
  • Image classification
  • Transfer learning

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Human-Computer Interaction
  • Information Systems

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  • Cite this

    Tian, H., Chen, S. C., Shyu, M. L., & Rubin, S. H. (2019). Automated neural network construction with similarity sensitive evolutionary algorithms. In Proceedings - 2019 IEEE 20th International Conference on Information Reuse and Integration for Data Science, IRI 2019 (pp. 283-290). [8843445] (Proceedings - 2019 IEEE 20th International Conference on Information Reuse and Integration for Data Science, IRI 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IRI.2019.00052