A Developmental Artificial Neural Network Model for Solving Multiple Problems

Created by W.Langdon from gp-bibliography.bib Revision:1.4348

@InProceedings{Miller:2017:GECCO,
  author =       "Julian F. Miller and Dennis G. Wilson",
  title =        "A Developmental Artificial Neural Network Model for
                 Solving Multiple Problems",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference Companion",
  series =       "GECCO '17",
  year =         "2017",
  isbn13 =       "978-1-4503-4939-0",
  address =      "Berlin, Germany",
  pages =        "69--70",
  size =         "2 pages",
  URL =          "http://doi.acm.org/10.1145/3067695.3075976",
  DOI =          "doi:10.1145/3067695.3075976",
  acmid =        "3075976",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  keywords =     "genetic algorithms, genetic programming, cartesian
                 genetic programming, artificial neural networks,
                 classification",
  month =        "15-19 " # jul,
  abstract =     "A developmental model of an artificial neuron is
                 presented. In this model, a pair of neural
                 developmental programs develop an entire artificial
                 neural network of arbitrary size. The pair of neural
                 chromosomes are evolved using Cartesian Genetic
                 Programming. During development, neurons and their
                 connections can move, change, die or be created. We
                 show that this two-chromosome genotype can be evolved
                 to develop into a single neural network from which
                 multiple conventional artificial neural networks can be
                 extracted. The extracted conventional ANNs share some
                 neurons across tasks. We have evaluated the performance
                 of this method on three standard classification
                 problems. The evolved pair of neuron programs can
                 generate artificial neural networks that perform
                 reasonably well on all three benchmark problems
                 simultaneously. It appears to be the first attempt to
                 solve multiple standard classification problems using a
                 developmental approach.",
  notes =        "Also known as \cite{Miller:2017:DAN:3067695.3075976}
                 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 Julian F Miller Dennis G Wilson

Citations