Genetic-Programming Based Prediction of Data Compression Saving

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

@InProceedings{Kattan:2009:EA,
  author =       "Ahmed Kattan and Riccardo Poli",
  title =        "Genetic-Programming Based Prediction of Data
                 Compression Saving",
  booktitle =    "9th International Conference, Evolution Artificielle,
                 EA 2009",
  year =         "2009",
  editor =       "Pierre Collet and Nicolas Monmarche and 
                 Pierrick Legrand and Marc Schoenauer and Evelyne Lutton",
  volume =       "5975",
  series =       "Lecture Notes in Computer Science",
  pages =        "182--193",
  address =      "Strasbourg, France",
  month =        oct # " 26-28",
  publisher =    "Springer",
  note =         "Revised Selected Papers",
  keywords =     "genetic algorithms, genetic programming, Compression,
                 Byte frequency distribution, Decision tree",
  isbn13 =       "978-3-642-14155-3",
  DOI =          "doi:10.1007/978-3-642-14156-0",
  size =         "12 pages",
  abstract =     "We use Genetic Programming (GP) to generate programs
                 that predict the data compression ratio for compression
                 algorithms. GP evolves programs with multiple
                 components. One component analyses statistical features
                 extracted from the files' byte frequency distribution
                 to come up with a compression ratio prediction. Another
                 component does the same but by analysing statistical
                 features extracted from the files' raw ASCII
                 representation. A further (evolved) component acts as a
                 decision tree to determine the overall output
                 (compression ratio estimation) returned by an
                 individual. The decision tree produces its result based
                 on a series of comparisons among statistical features
                 extracted from the files and the outputs of the two
                 prediction components. The evolved decision tree has
                 the choice to select either the outputs of the two
                 compression prediction trees or alternatively, to
                 integrate them into an evolved mathematical formula.
                 Experiments with the proposed approach show that GP is
                 able to accurately estimate the compression ratio of
                 unseen files thereby avoiding the need to run multiple
                 compressions on a file to decide which one provide best
                 results.",
  notes =        "EA'09 Published 2010",
}

Genetic Programming entries for Ahmed Kattan Riccardo Poli

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