Flash: A GP-GPU Ensemble Learning System for Handling Large Datasets

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{arnaldo:2014:EuroGP,
  author =       "Ignacio Arnaldo and Kalyan Veeramachaneni and 
                 Una-May O'Reilly",
  title =        "Flash: A GP-GPU Ensemble Learning System for Handling
                 Large Datasets",
  booktitle =    "17th European Conference on Genetic Programming",
  year =         "2014",
  editor =       "Miguel Nicolau and Krzysztof Krawiec and 
                 Malcolm I. Heywood and Mauro Castelli and Pablo Garcia-Sanchez and 
                 Juan J. Merelo and Victor M. {Rivas Santos} and 
                 Kevin Sim",
  series =       "LNCS",
  volume =       "8599",
  publisher =    "Springer",
  pages =        "13--24",
  address =      "Granada, Spain",
  month =        "23-25 " # apr,
  organisation = "EvoStar",
  keywords =     "genetic algorithms, genetic programming, GPU",
  isbn13 =       "978-3-662-44302-6",
  DOI =          "DOI:10.1007/978-3-662-44303-3_2",
  abstract =     "The Flash system runs ensemble-based Genetic
                 Programming (GP) symbolic regression on a shared memory
                 desktop. To significantly reduce the high time cost of
                 the extensive model predictions required by symbolic
                 regression, its fitness evaluations are tasked to the
                 desktop's GPU. Successive GP {"}instances{"} are run on
                 different data subsets and randomly chosen objective
                 functions. Best models are collected after a fixed
                 number of generations and then fused with an adaptive,
                 output-space method. New instance launches are halted
                 once learning is complete. We demonstrate that Flash's
                 ensemble strategy not only makes GP more robust, but it
                 also provides an informed online means of halting the
                 learning process. Flash enables GP to learn from a
                 dataset composed of 370K exemplars and 90 features,
                 evolving a population of 1000 individuals over 100
                 generations in as few as 50 seconds.",
  notes =        "Part of \cite{Nicolau:2014:GP} EuroGP'2014 held in
                 conjunction with EvoCOP2014, EvoBIO2014, EvoMusArt2014
                 and EvoApplications2014",
}

Genetic Programming entries for Ignacio Arnaldo Lucas Kalyan Veeramachaneni Una-May O'Reilly

Citations