{
  "id": "1706.08493",
  "version": "v1",
  "published": "2017-06-26T17:34:14.000Z",
  "updated": "2017-06-26T17:34:14.000Z",
  "title": "Towards the Evolution of Multi-Layered Neural Networks: A Dynamic Structured Grammatical Evolution Approach",
  "authors": [
    "Filipe Assunção",
    "Nuno Lourenço",
    "Penousal Machado",
    "Bernardete Ribeiro"
  ],
  "categories": [
    "cs.NE",
    "cs.AI"
  ],
  "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 neurons.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2017-06-26T17:34:14.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "dynamic structured grammatical evolution approach",
      "multi-layered neural networks",
      "output neuron",
      "multiple output nodes",
      "artificial neural networks"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}