On GPU Based Fitness Evaluation with Decoupled Training Partition Cardinality

Created by W.Langdon from gp-bibliography.bib Revision:1.4549

  author =       "Jazz Alyxzander Turner-Baggs and Malcolm I. Heywood",
  title =        "On GPU Based Fitness Evaluation with Decoupled
                 Training Partition Cardinality",
  booktitle =    "Applications of Evolutionary Computing,
                 EvoApplications 2013: EvoCOMNET, EvoCOMPLEX, EvoENERGY,
                 EvoFIN, EvoGAMES, EvoIASP, EvoINDUSTRY, EvoNUM, EvoPAR,
                 EvoRISK, EvoROBOT, EvoSTOC",
  year =         "2013",
  month =        "3-5 " # apr,
  editor =       "Anna I. Esparcia-Alcazar and Antonio Della Cioppa and 
                 Ivanoe {De Falco} and Ernesto Tarantino and 
                 Carlos Cotta and Robert Schaefer and Konrad Diwold and 
                 Kyrre Glette and Andrea Tettamanzi and 
                 Alexandros Agapitos and Paolo Burrelli and J. J. Merelo and 
                 Stefano Cagnoni and Mengjie Zhang and Neil Urquhart and Kevin Sim and 
                 Aniko Ekart and Francisco {Fernandez de Vega} and 
                 Sara Silva and Evert Haasdijk and Gusz Eiben and 
                 Anabela Simoes and Philipp Rohlfshagen",
  series =       "LNCS",
  volume =       "7835",
  publisher =    "Springer Verlag",
  address =      "Vienna",
  publisher_address = "Berlin",
  pages =        "489--498",
  organisation = "EvoStar",
  keywords =     "genetic algorithms, genetic programming, GPGPU, SBB",
  isbn13 =       "978-3-642-37191-2",
  DOI =          "doi:10.1007/978-3-642-37192-9_49",
  size =         "10 pages",
  abstract =     "GPU acceleration of increasingly complex variants of
                 evolutionary frameworks typically assume that all the
                 training data used during evolution resides on the GPU.
                 Such an assumption places limits on the style of
                 application to which evolutionary computation can be
                 applied. Conversely, several coevolutionary frameworks
                 explicitly decouple fitness evaluation from the size of
                 the training partition. Thus, a subset of training
                 exemplars is coevolved with the population of evolved
                 individuals. In this work we articulate the design
                 decisions necessary to support Pareto archiving for
                 Genetic Programming under a commodity GPU platform.
                 Benchmarking of corresponding CPU and GPU
                 implementations demonstrates that the GPU platform is
                 still capable of providing a times ten reduction in
                 computation time.",
  notes =        "nVidia GTX 660Ti. KDD 1999, Shuttle. Does not give
                 speed in terms of GP operations per second GPops

                 EvoApplications2013 held in conjunction with
                 EuroGP2013, EvoCOP2013, EvoBio'2013 and EvoMusArt2013",

Genetic Programming entries for Jazz Alyxzander Turner-Baggs Malcolm Heywood