Fast Knowledge Discovery in Time Series with GPGPU on Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4208

  author =       "Sungjoo Ha and Byung-Ro Moon",
  title =        "Fast Knowledge Discovery in Time Series with GPGPU on
                 Genetic Programming",
  booktitle =    "GECCO '15: Proceedings of the 2015 Annual Conference
                 on Genetic and Evolutionary Computation",
  year =         "2015",
  editor =       "Sara Silva and Anna I Esparcia-Alcazar and 
                 Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and 
                 Christine Zarges and Luis Correia and Terence Soule and 
                 Mario Giacobini and Ryan Urbanowicz and 
                 Youhei Akimoto and Tobias Glasmachers and 
                 Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and 
                 Marta Soto and Carlos Cotta and Francisco B. Pereira and 
                 Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and 
                 Heike Trautmann and Jean-Baptiste Mouret and 
                 Sebastian Risi and Ernesto Costa and Oliver Schuetze and 
                 Krzysztof Krawiec and Alberto Moraglio and 
                 Julian F. Miller and Pawel Widera and Stefano Cagnoni and 
                 JJ Merelo and Emma Hart and Leonardo Trujillo and 
                 Marouane Kessentini and Gabriela Ochoa and Francisco Chicano and 
                 Carola Doerr",
  isbn13 =       "978-1-4503-3472-3",
  pages =        "1159--1166",
  keywords =     "genetic algorithms, genetic programming, Parallel
                 Evolutionary Systems",
  month =        "11-15 " # jul,
  organisation = "SIGEVO",
  address =      "Madrid, Spain",
  URL =          "",
  DOI =          "doi:10.1145/2739480.2754669",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "We tackle the problem of knowledge discovery in time
                 series data using genetic programming and GPGPUs. Using
                 genetic programming, various precursor patterns that
                 have certain attractive qualities are evolved to
                 predict the events of interest. Unfortunately, evolving
                 a set of diverse patterns typically takes huge
                 execution time, sometimes longer than one month for
                 this case. In this paper, we address this problem by
                 proposing a parallel GP framework using GPGPUs,
                 particularly in the context of big financial data. By
                 maximally exploiting the structure of the nVidia GPGPU
                 platform on stock market time series data, we were able
                 see more than 250-fold reduction in the running time.",
  notes =        "Also known as \cite{2754669} GECCO-2015 A joint
                 meeting of the twenty fourth international conference
                 on genetic algorithms (ICGA-2015) and the twentith
                 annual genetic programming conference (GP-2015)",

Genetic Programming entries for Sungjoo Ha Byung-Ro Moon