On Evolutionary Approximation of Sigmoid Function for HW/SW Embedded Systems

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

  author =       "Milos Minarik and Lukas Sekanina",
  title =        "On Evolutionary Approximation of Sigmoid Function for
                 HW/SW Embedded Systems",
  booktitle =    "EuroGP 2017: Proceedings of the 20th European
                 Conference on Genetic Programming",
  year =         "2017",
  month =        "19-21 " # apr,
  editor =       "Mauro Castelli and James McDermott and 
                 Lukas Sekanina",
  series =       "LNCS",
  volume =       "10196",
  publisher =    "Springer Verlag",
  address =      "Amsterdam",
  pages =        "343--358",
  organisation = "species",
  keywords =     "genetic algorithms, genetic programming: Poster",
  DOI =          "doi:10.1007/978-3-319-55696-3_22",
  abstract =     "Providing machine learning capabilities on low cost
                 electronic devices is a challenging goal especially in
                 the context of the Internet of Things paradigm. In
                 order to deliver high performance machine intelligence
                 on low power devices, suitable hardware accelerators
                 have to be introduced. In this paper, we developed a
                 method enabling to evolve a hardware implementation
                 together with a corresponding software controller for
                 key components of smart embedded systems. The proposed
                 approach is based on a multi-objective design space
                 exploration conducted by means of extended linear
                 genetic programming. The approach was evaluated in the
                 task of approximate sigmoid function design which is an
                 important component of hardware implementations of
                 neural networks. During these experiments, we
                 automatically rediscovered some approximate sigmoid
                 functions known from the literature. The method was
                 implemented as an extension of an existing platform
                 supporting concurrent evolution of hardware and
                 software of embedded systems.",
  notes =        "Part of \cite{Castelli:2017:GP} EuroGP'2017 held
                 inconjunction with EvoCOP2017, EvoMusArt2017 and

Genetic Programming entries for Milos Minarik Lukas Sekanina