Genetic Programming of Augmenting Topologies for Hypercube-Based Indirect Encoding of Artificial Neural Networks

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{conf/softcomp/DrchalS12,
  author =       "Jan Drchal and Miroslav Snorek",
  title =        "Genetic Programming of Augmenting Topologies for
                 Hypercube-Based Indirect Encoding of Artificial Neural
                 Networks",
  booktitle =    "7th International Conference, Soft Computing Models in
                 Industrial and Environmental Applications SOCO-2012",
  year =         "2013",
  editor =       "Vaclav Snasel and Ajith Abraham and 
                 Emilio S. Corchado",
  volume =       "188",
  series =       "Advances in Intelligent Systems and Computing",
  pages =        "63--72",
  address =      "Ostrava, Czech Republic",
  month =        sep # " 5th-7th",
  publisher =    "Springer",
  bibdate =      "2013-01-16",
  bibsource =    "DBLP,
                 http://dblp.uni-trier.de/db/conf/softcomp/soco2012.html#DrchalS12",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-3-642-32921-0",
  DOI =          "doi:10.1007/978-3-642-32922-7_7",
  abstract =     "n this paper we present a novel algorithm called GPAT
                 (Genetic Programming of Augmenting Topologies) which
                 evolves Genetic Programming (GP) trees in a similar way
                 as a well-established neuro-evolutionary algorithm NEAT
                 (NeuroEvolution of Augmenting Topologies) does. The
                 evolution starts from a minimal form and gradually adds
                 structure as needed. A niching evolutionary algorithm
                 is used to protect individuals of a variable complexity
                 in a single population. Although GPAT is a general
                 approach we employ it mainly to evolve artificial
                 neural networks by means of Hypercube-based indirect
                 encoding which is an approach allowing for evolution of
                 large-scale neural networks having theoretically
                 unlimited size. We perform also experiments for
                 directly encoded problems. The results show that GPAT
                 outperforms both GP and NEAT taking the best of both.",
}

Genetic Programming entries for Jan Drchal Miroslav Snorek

Citations