A Multi-Objective Grammatical Evolution Framework to Generate Convolutional Neural Network Architectures

Created by W.Langdon from gp-bibliography.bib Revision:1.8129

@InProceedings{daSilva:2021:CEC,

• author = "Cleber A. C. F. {da Silva} and Daniel {Carneiro Rosa} and Pericles B. C. Miranda and Filipe R. Cordeiro and Tapas Si and Andre C. A. Nascimento and Rafael F. L. Mello and Paulo S. G. {de Mattos Neto}",
• booktitle = "2021 IEEE Congress on Evolutionary Computation (CEC)",
• title = "A Multi-Objective Grammatical Evolution Framework to Generate Convolutional Neural Network Architectures",
• year = "2021",
• editor = "Yew-Soon Ong",
• pages = "2187--2194",
• address = "Krakow, Poland",
• month = "28 " # jun # "-1 " # jul,
• keywords = "genetic algorithms, genetic programming, Grammatical Evolution, ANN, Computer vision, Computer architecture, Evolutionary computation, Network architecture, Grammar, Convolutional neural networks, Optimization, Deep Neural Networks, Multi-objective optimization",
• isbn13 = "978-1-7281-8393-0",
• DOI = "doi:10.1109/CEC45853.2021.9504822",
• abstract = "Deep Convolutional Neural Networks (CNNs) have reached the attention in the last decade due to their successful application to many computer vision domains. Several handcrafted architectures have been proposed in the literature, with increasing depth and millions of parameters. However, the optimal architecture size and parameters setup are dataset-dependent and challenging to find. For addressing this problem, this work proposes a Multi-Objective Grammatical Evolution framework to automatically generate suitable CNN architectures (layers and parameters) for a given classification problem. For this, a Context-free Grammar is developed, representing the search space of possible CNN architectures. The proposed method seeks to find suitable network architectures considering two objectives: accuracy and F1-score. We evaluated our method on CIFAR-10, and the results obtained show that our method generates simpler CNN architectures and overcomes the results achieved by larger (more complex) state-of-the-art CNN approaches and other grammars.",
• notes = "Also known as \cite{9504822}",

}

Genetic Programming entries for Cleber A C F da Silva Daniel Carneiro Rosa Pericles Barbosa Miranda Filipe Rolim Cordeiro Tapas Si Andre C A Nascimento Rafael F L Mello Paulo S G de Mattos Neto

Citations