A Grammar-based Genetic Programming Approach to Optimize Convolutional Neural Network Architectures

  • Jessica Barbosa Diniz UFRPE
  • Filipe R. Cordeiro UFRPE
  • Pericles B. C. Miranda UFRPE
  • Laura A. Tomaz da Silva PUCRS


Deep Learning is a research area under the spotlight in recent years due to its successful application to many domains, such as computer vision and image recognition. The most prominent technique derived from Deep Learning is Convolutional Neural Network, which allows the network to automatically learn representations needed for detection or classification tasks. However, Convolutional Neural Networks have some limitations, as designing these networks are not easy to master and require expertise and insight. In this work, we present the use of Genetic Algorithm associated to Grammar-based Genetic Programming to optimize Convolution Neural Network architectures. To evaluate our proposed approach, we adopted CIFAR-10 dataset to validate the evolution of the generated architectures, using the metric of accuracy to evaluate its classification performance in the test dataset. The results demonstrate that our method using Grammar-based Genetic Programming can easily produce optimized CNN architectures that are competitive and achieve high accuracy results.


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DINIZ, Jessica Barbosa; CORDEIRO, Filipe R.; MIRANDA, Pericles B. C.; DA SILVA, Laura A. Tomaz. A Grammar-based Genetic Programming Approach to Optimize Convolutional Neural Network Architectures. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 15. , 2018, São Paulo. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2018 . p. 82-93. ISSN 2763-9061. DOI: https://doi.org/10.5753/eniac.2018.4406.

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