A Genetic Programming Approach to Designing Convolutional Neural Network Architectures

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "Masanori Suganuma and Shinichi Shirakawa and 
                 Tomoharu Nagao",
  title =        "A Genetic Programming Approach to Designing
                 Convolutional Neural Network Architectures",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  series =       "GECCO '17",
  year =         "2017",
  isbn13 =       "978-1-4503-4920-8",
  address =      "Berlin, Germany",
  pages =        "497--504",
  size =         "8 pages",
  URL =          "http://doi.acm.org/10.1145/3071178.3071229",
  DOI =          "doi:10.1145/3071178.3071229",
  acmid =        "3071229",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  keywords =     "genetic algorithms, genetic programming, convolutional
                 neural network, deep learning, designing neural network
  month =        "15-19 " # jul,
  abstract =     "The convolutional neural network (CNN), which is one
                 of the deep learning models, has seen much success in a
                 variety of computer vision tasks. However, designing
                 CNN architectures still requires expert knowledge and a
                 lot of trial and error. In this paper, we attempt to
                 automatically construct CNN architectures for an image
                 classification task based on Cartesian genetic
                 programming (CGP). In our method, we adopt highly
                 functional modules, such as convolutional blocks and
                 tensor concatenation, as the node functions in CGP. The
                 CNN structure and connectivity represented by the CGP
                 encoding method are optimized to maximize the
                 validation accuracy. To evaluate the proposed method,
                 we constructed a CNN architecture for the image
                 classification task with the CIFAR-10 dataset. The
                 experimental result shows that the proposed method can
                 be used to automatically find the competitive CNN
                 architecture compared with state-of-the-art models.",
  notes =        "Also known as \cite{Suganuma:2017:GPA:3071178.3071229}
                 GECCO-2017 A Recombination of the 26th International
                 Conference on Genetic Algorithms (ICGA-2017) and the
                 22nd Annual Genetic Programming Conference (GP-2017)",

Genetic Programming entries for Masanori Suganuma Shinichi Shirakawa Tomoharu Nagao