Automatic generation of neural networks with structured Grammatical Evolution

Created by W.Langdon from gp-bibliography.bib Revision:1.4549

  author =       "Filipe Assuncao and Nuno Lourenco and 
                 Penousal Machado and Bernardete Ribeiro",
  booktitle =    "2017 IEEE Congress on Evolutionary Computation (CEC)",
  title =        "Automatic generation of neural networks with
                 structured Grammatical Evolution",
  year =         "2017",
  pages =        "1557--1564",
  abstract =     "The effectiveness of Artificial Neural Networks (ANNs)
                 depends on a non-trivial manual crafting of their
                 topology and parameters. Typically, practitioners
                 resort to a time consuming methodology of
                 trial-and-error to find and/or adjust the models to
                 solve specific tasks. To minimise this burden one might
                 resort to algorithms for the automatic selection of the
                 most appropriate properties of a given ANN. A
                 remarkable example of such methodologies is
                 Grammar-based Genetic Programming. This work analyses
                 and compares the use of two grammar-based methods,
                 Grammatical Evolution (GE) and Structured Grammatical
                 Evolution (SGE), to automatically design and configure
                 ANNs. The evolved networks are used to tackle several
                 classification datasets. Experimental results show that
                 SGE is able to automatically build better models than
                 GE, and that are competitive with the state of the art,
                 outperforming hand-designed ANNs in all the used
  keywords =     "genetic algorithms, genetic programming, Grammatical
  DOI =          "doi:10.1109/CEC.2017.7969488",
  month =        jun,
  notes =        "Also known as \cite{7969488}",

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