Structurally Layered Representation Learning: Towards Deep Learning through Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4175

@InProceedings{Rodriguez-Coayahuitl:2018:EuroGP,
  author =       "Lino Rodriguez-Coayahuitl and Alicia Morales-Reyes and 
                 Hugo Jair Escalante",
  title =        "Structurally Layered Representation Learning: Towards
                 Deep Learning through Genetic Programming",
  booktitle =    "EuroGP 2018: Proceedings of the 21st European
                 Conference on Genetic Programming",
  year =         "2018",
  month =        "4-6 " # apr,
  editor =       "Mauro Castelli and Lukas Sekanina and 
                 Mengjie Zhang and Stefano Cagnoni and Pablo Garcia-Sanchez",
  series =       "LNCS",
  volume =       "10781",
  publisher =    "Springer Verlag",
  address =      "Parma, Italy",
  pages =        "271--288",
  organisation = "EvoStar, Species",
  keywords =     "genetic algorithms, genetic programming: Poster",
  isbn13 =       "978-3-319-77552-4",
  DOI =          "doi:10.1007/978-3-319-77553-1_17",
  abstract =     "We introduce a novel method for representation
                 learning based on genetic programming (GP). Inspired
                 into the way that deep neural networks learn
                 descriptive/discriminative representations from raw
                 data, we propose a structurally layered representation
                 that allows GP to learn a feature space from large
                 scale and high dimensional data sets. Previous efforts
                 from the GP community for feature learning have focused
                 on small data sets with a few input variables, also,
                 most approaches rely on domain expert knowledge to
                 produce useful representations. In this paper, we
                 introduce the structurally layered GP formulation,
                 together with an efficient scheme to explore the search
                 space and show that this framework can be used to learn
                 representations from large data sets of high
                 dimensional raw data. As case of study we describe the
                 implementation and experimental evaluation of an
                 autoencoder developed under the proposed framework.
                 Results evidence the benefits of the proposed framework
                 and pave the way for the development of deep genetic
                 programming.",
  notes =        "See also https://arxiv.org/abs/1802.07133

                 Part of \cite{Castelli:2018:GP} EuroGP'2018 held in
                 conjunction with EvoCOP2018, EvoMusArt2018 and
                 EvoApplications2018",
}

Genetic Programming entries for Lino Rodriguez-Coayahuitl Alicia Morales-Reyes Hugo Jair Escalante

Citations