DENSER: deep evolutionary network structured representation

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@Article{Assuncao:2019:GPEM,
  author =       "Filipe Assuncao and Nuno Lourenco and 
                 Penousal Machado and Bernardete Ribeiro",
  title =        "{DENSER}: deep evolutionary network structured
                 representation",
  journal =      "Genetic Programming and Evolvable Machines",
  note =         "Online first",
  keywords =     "genetic algorithms, genetic programming, Grammatical
                 Evolution",
  ISSN =         "1389-2576",
  DOI =          "doi:",
  size =         "31 pages",
  abstract =     "Deep evolutionary network structured representation
                 (DENSER) is a novel evolutionary approach for the
                 automatic generation of deep neural networks (DNNs)
                 which combines the principles of genetic algorithms
                 (GAs) with those of dynamic structured grammatical
                 evolution (DSGE). The GA-level encodes the macro
                 structure of evolution, i.e., the layers, learning,
                 and/or data augmentation methods (among others); the
                 DSGE-level specifies the parameters of each GA
                 evolutionary unit and the valid range of the
                 parameters. The use of a grammar makes DENSER a general
                 purpose framework for generating DNNs: one just needs
                 to adapt the grammar to be able to deal with different
                 network and layer types, problems, or even to change
                 the range of the parameters. DENSER is tested on the
                 automatic generation of convolutional neural networks
                 (CNNs) for the CIFAR-10 dataset, with the best
                 performing networks reaching accuracies of up to
                 95.22percent. Furthermore, we take the fittest networks
                 evolved on the CIFAR-10, and apply them to classify
                 MNIST, Fashion-MNIST, SVHN, Rectangles, and CIFAR-100.
                 The results show that the DNNs discovered by DENSER
                 during evolution generalise, are robust, and scale. The
                 most impressive result is the 78.75percent
                 classification accuracy on the CIFAR-100 dataset,
                 which, to the best of our knowledge, sets a new
                 state-of-the-art on methods that seek to automatically
                 design CNNs.",
}

Genetic Programming entries for Filipe Assuncao Nuno Lourenco Penousal Machado Bernardete Ribeiro

Citations