Solving Classification Problems Using Genetic Programming Algorithms on GPUs

Created by W.Langdon from gp-bibliography.bib Revision:1.4192

  author =       "Alberto Cano and Amelia Zafra and Sebastian Ventura",
  title =        "Solving Classification Problems Using Genetic
                 Programming Algorithms on GPUs",
  booktitle =    "Hybrid Artificial Intelligence Systems",
  year =         "2010",
  series =       "Lecture Notes in Computer Science",
  editor =       "Emilio Corchado and Manuel {Grana Romay} and 
                 Alexandre {Manhaes Savio}",
  publisher =    "Springer",
  pages =        "17--26",
  volume =       "6077",
  address =      "San Sebastian, Spain",
  month =        jun # " 23-25",
  DOI =          "doi:10.1007/978-3-642-13803-4_3",
  email =        "",
  keywords =     "genetic algorithms, genetic programming, gpu, gpgpu,
  size =         "10 pages",
  abstract =     "Genetic Programming is very efficient in problem
                 solving compared to other proposals but its performance
                 is very slow when the size of the data increases. This
                 paper proposes a model for multi-threaded Genetic
                 Programming classification evaluation using a NVIDIA
                 CUDA GPUs programming model to parallelise the
                 evaluation phase and reduce computational time. Three
                 different well-known Genetic Programming classification
                 algorithms are evaluated using the parallel evaluation
                 model proposed. Experimental results using UCI Machine
                 Learning data sets compare the performance of the three
                 classification algorithms in single and multithreaded
                 Java, C and CUDA GPU code. Results show that our
                 proposal is much more efficient.",
  affiliation =  "University of Cordoba Department of Computing and
                 Numerical Analysis 14071 Cordoba Spain",
  notes =        "JCLEC. No absolute speed measure given (cf.
                 \cite{langdon:2008:eurogp}). confusion matrix
                 calculated on two GTX 285. big multi-class training
                 sets from UCI (poker and shuttle) comparison with Java
                 and Intel i7 multi-core. Three GP fitness functions.
                 RPN interpreter",

Genetic Programming entries for Alberto Cano Rojas Amelia Zafra Gomez Sebastian Ventura