Time series forecasting using massively parallel genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4216

  author =       "Sven E Eklund",
  title =        "Time series forecasting using massively parallel
                 genetic programming",
  booktitle =    "Proceedings of Parallel and Distributed Processing
                 International Symposium",
  year =         "2003",
  pages =        "143--147",
  month =        "22-26 " # apr,
  organisation = "IEEE",
  keywords =     "genetic algorithms, genetic programming, EHW, FPGA,
                 Virtex XC2V10000, wolfe sunspot",
  DOI =          "doi:10.1109/IPDPS.2003.1213272",
  URL =          "http://dalea.du.se/research/?itemId=147",
  abstract =     "a massively parallel GP model in hardware as an
                 efficient,flexible and scaleable machine learning
                 system.This fine-grained diffusion architecture
                 consists of a large amount of independent processing
                 nodes that evolve a large number of small, overlapping
                 subpopulations.Every node has an embedded CPU that
                 executes a linear machine code GP representation at a
                 rate of up to 20,000 generations per second.Besides
                 being efficient,implementing the system in VLSI makes
                 it highly portable and makes it possible to target
                 mobile,n-line applications.The SIMD-like architecture
                 also makes the system scalable so that larger problems
                 can be addressed with a system with more processing
                 nodes.Finally,the use of GP representation and VHDL
                 modeling makes the system highly flexible and easy to
                 adapt to different applications.We demonstrate the
                 effectiveness of the system on a time series
                 forecasting application.",
  notes =        "outperforms SETAR but not best ANN",

Genetic Programming entries for Sven E Eklund