Automatic Random Tree Generator on FPGA

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "Carlos Goribar and Yazmin Maldonado and 
                 Leonardo Trujillo",
  title =        "Automatic Random Tree Generator on {FPGA}",
  booktitle =    "NEO 2015: Results of the Numerical and Evolutionary
                 Optimization Workshop NEO 2015 held at September 23-25
                 2015 in Tijuana, Mexico",
  year =         "2015",
  editor =       "Oliver Schuetze and Leonardo Trujillo and 
                 Pierrick Legrand and Yazmin Maldonado",
  volume =       "663",
  series =       "Studies in Computational Intelligence",
  chapter =      "4",
  pages =        "89--104",
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming, EHW, FPGA,
  isbn13 =       "978-3-319-44003-3",
  DOI =          "doi:10.1007/978-3-319-44003-3_4",
  abstract =     "In this work we propose the implementation of an
                 automatic random tree generator on an FPGA for genetic
                 programming (GP). While most authors in specialized
                 literature avoid the use of the tree data structure in
                 their implementations of GP on Field Programmable Gate
                 Arrays (FPGAs), due to the impossibility of using
                 pointers (references) in the Very High Speed Integrated
                 Circuit Hardware Description Language (VHDL), we
                 propose two methods for a single matrix implementation
                 and one for a vector implementation. All trees in the
                 population are created in concurrent processes leading
                 to significant time savings. We present pseudocode and
                 results of hardware consumption for matrix and vector
                 implementations. Results show that up to 100 trees can
                 be implemented in a Spartan-6 FPGA using the
                 representation of one tree in a single matrix in
                 parallel processes. Moreover, this implementation
                 requires less resources than the apparently simpler
                 vector representation.",
  notes =        "Published 2017. See also

Genetic Programming entries for Carlos Antonio Goribar Jimenez Yazmin Maldonado Robles Leonardo Trujillo