Scaling Genetic Programming for Data Classification using MapReduce Methodology

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

@InProceedings{Al-Madi:2013:nabic,
  author =       "Nailah Al-Madi and Simone A. Ludwig",
  title =        "Scaling Genetic Programming for Data Classification
                 using MapReduce Methodology",
  booktitle =    "5th World Congress on Nature and Biologically Inspired
                 Computing",
  year =         "2013",
  editor =       "Simone Ludwig and Patricia Melin and Ajith Abraham and 
                 Ana Maria Madureira and Kendall Nygard and 
                 Oscar Castillo and Azah Kamilah Muda and Kun Ma and 
                 Emilio Corchado",
  pages =        "132--139",
  address =      "Fargo, USA",
  month =        "12-14 " # aug,
  publisher =    "IEEE",
  keywords =     "genetic algorithms, genetic programming, Evolutionary
                 computation, data classification, Parallel Processing,
                 MapReduce, Hadoop",
  isbn13 =       "978-1-4799-1415-9",
  URL =          "http://www.mirlabs.net/nabic13/proceedings/html/paper34.xml",
  DOI =          "doi:10.1109/NaBIC.2013.6617851",
  size =         "8 pages",
  abstract =     "Genetic Programming (GP) is an optimisation method
                 that has proved to achieve good results. It solves
                 problems by generating programs and applying natural
                 operations on these programs until a good solution is
                 found. GP has been used to solve many classifications
                 problems, however, its drawback is the long execution
                 time. When GP is applied on the classification task,
                 the execution time proportionally increases with the
                 dataset size. Therefore, to manage the long execution
                 time, the GP algorithm is parallelised in order to
                 speed up the classification process. Our GP is
                 implemented based on the MapReduce methodology
                 (abbreviated as MRGP), in order to benefit from the
                 MapReduce concept in terms of fault tolerance, load
                 balancing, and data locality. MRGP does not only
                 accelerate the execution time of GP for large datasets,
                 it also provides the ability to use large population
                 sizes, thus finding the best result in fewer numbers of
                 generations. MRGP is evaluated using different
                 population sizes ranging from 1,000 to 100,000
                 measuring the accuracy, scalability, and speedup",
  notes =        "USB only?, IEEE Catalog Number: CFP1395H-POD Also
                 known as \cite{6617851}",
}

Genetic Programming entries for Nailah Al-Madi Simone A Ludwig

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