Parallel multi-objective Ant Programming for classification using GPUs

Created by W.Langdon from gp-bibliography.bib Revision:1.3949

@Article{Cano:2013:JPDC,
  author =       "Alberto Cano and Juan Luis Olmo and 
                 Sebastian Ventura",
  title =        "Parallel multi-objective Ant Programming for
                 classification using {GPUs}",
  journal =      "Journal of Parallel and Distributed Computing",
  year =         "2013",
  volume =       "73",
  number =       "6",
  pages =        "713--728",
  month =        jun,
  keywords =     "genetic algorithms, genetic programming, GPU, Reverse
                 Polish RPN, grammar based, Ant programming (AP), Ant
                 colony optimisation (ACO), Parallel computing,
                 Classification",
  ISSN =         "0743-7315",
  DOI =          "doi:10.1016/j.jpdc.2013.01.017",
  size =         "16 pages",
  abstract =     "Classification using Ant Programming is a challenging
                 data mining task which demands a great deal of
                 computational resources when handling data sets of high
                 dimensionality. This paper presents a new
                 parallelisation approach of an existing multi-objective
                 Ant Programming model for classification, using GPUs
                 and the nVidia CUDA programming model. The
                 computational costs of the different steps of the
                 algorithm are evaluated and it is discussed how best to
                 parallelise them. The features of both the CPU parallel
                 and GPU versions of the algorithm are presented. An
                 experimental study is carried out to evaluate the
                 performance and efficiency of the interpreter of the
                 rules, and reports the execution times and speedups
                 regarding variable population size, complexity of the
                 rules mined and dimensionality of the data sets.
                 Experiments measure the original single-threaded and
                 the new multi-threaded CPU and GPU times with different
                 number of GPU devices. The results are reported in
                 terms of the number of Giga GP operations per second of
                 the interpreter (up to 10 billion GPops/s) and the
                 speedup achieved (up to 834 times vs CPU, 212 times vs
                 4-threaded CPU). The proposed GPU model is demonstrated
                 to scale efficiently to larger datasets and to multiple
                 GPU devices, which allows the expansion of its
                 applicability to significantly more complicated data
                 sets, previously unmanageable by the original algorithm
                 in reasonable time.",
  notes =        "genetic programming. GPU GPops/second given for
                 interpreter only. Two nVidia GPUs (GTX 285, 480) per
                 host PC. Ubuntu Linux CUDA 4.2. Occupancy. UCI poker,
                 etc. Evolved decision rules. (One per output class?)
                 Host parallel code uses Java threads. Rules in constant
                 memory, stack in local (off-chip) memory (L1/L2
                 cache).",
}

Genetic Programming entries for Alberto Cano Rojas Juan Luis Olmo Sebastian Ventura

Citations