Evolutionary Programming Based Deep Learning Feature Selection and Network Construction for Visual Data Classification

Haiman Tian, Shu Ching Chen, Mei Ling Shyu

Research output: Contribution to journalReview articlepeer-review

5 Scopus citations


Convolutional Neural Network (CNN) models and many accessible large-scale public visual datasets have brought lots of research work to a remarkable new stage. Benefited from well-trained CNN models, small training datasets can learn comprehensive features by utilizing the preliminary features from transfer learning. However, the performance is not guaranteed when taking these features to construct a new model, as the differences always exist between the source and target domains. In this paper, we propose to build an Evolution Programming-based framework to address various challenges. This framework automates both the feature learning and model building processes. It first identifies the most valuable features from pre-trained models and then constructs a suitable model to understand the characteristic features for different tasks. Each model differs in numerous ways. Overall, the experimental results effectively reach optimal solutions, demonstrating that a time-consuming task could also be conducted by an automated process that exceeds the human ability.

Original languageEnglish (US)
Pages (from-to)1053-1066
Number of pages14
JournalInformation Systems Frontiers
Issue number5
StatePublished - Oct 1 2020


  • Deep learning
  • Evolutionary programming
  • Image classification

ASJC Scopus subject areas

  • Software
  • Theoretical Computer Science
  • Information Systems
  • Computer Networks and Communications


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