A Genetic Programming Approach to Designing Convolutional Neural Network Architectures

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Suganuma:2017:GECCO,

• 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, Cartesian genetic programming, ANN, convolutional neural network, deep learning, designing neural network architectures",
• 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

Citations