Towards the Evolution of Multi-layered Neural Networks: A Dynamic Structured Grammatical Evolution Approach

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

  author =       "Filipe Assuncao and Nuno Lourenco and 
                 Penousal Machado and Bernardete Ribeiro",
  title =        "Towards the Evolution of Multi-layered Neural
                 Networks: A Dynamic Structured Grammatical Evolution
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  series =       "GECCO '17",
  year =         "2017",
  isbn13 =       "978-1-4503-4920-8",
  address =      "Berlin, Germany",
  pages =        "393--400",
  size =         "8 pages",
  URL =          "",
  DOI =          "doi:10.1145/3071178.3071286",
  acmid =        "3071286",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  keywords =     "genetic algorithms, genetic programming, grammatical
                 evolution, Artificial Neural Networks, Classification,
                 Grammar-based Genetic Programming, NeuroEvolution",
  month =        "15-19 " # jul,
  abstract =     "Current grammar-based NeuroEvolution approaches have
                 several shortcomings. On the one hand, they do not
                 allow the generation of Artificial Neural Networks
                 (ANNs) composed of more than one hidden-layer. On the
                 other, there is no way to evolve networks with more
                 than one output neuron. To properly evolve ANNs with
                 more than one hidden-layer and multiple output nodes
                 there is the need to know the number of neurons
                 available in previous layers. In this paper we
                 introduce Dynamic Structured Grammatical Evolution
                 (DSGE): a new genotypic representation that overcomes
                 the aforementioned limitations. By enabling the
                 creation of dynamic rules that specify the connection
                 possibilities of each neuron, the methodology enables
                 the evolution of multi-layered ANNs with more than one
                 output neuron. Results in different classification
                 problems show that DSGE evolves effective single and
                 multi-layered ANNs, with a varying number of output
  notes =        "Also known as
                 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 Filipe Assuncao Nuno Lourenco Penousal Machado Bernardete Ribeiro