Evolutionary synthesis of lossless compression algorithms with GP-zip3

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

@InProceedings{Kattan:2010:cec,
  author =       "Ahmed Kattan and Riccardo Poli",
  title =        "Evolutionary synthesis of lossless compression
                 algorithms with {GP-zip3}",
  booktitle =    "IEEE Congress on Evolutionary Computation (CEC 2010)",
  year =         "2010",
  address =      "Barcelona, Spain",
  month =        "18-23 " # jul,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-1-4244-6910-9",
  abstract =     "Here we propose GP-zip3, a system which uses Genetic
                 Programming to find optimal ways to combine standard
                 compression algorithms for the purpose of compressing
                 files and archives. GP-zip3 evolves programs with
                 multiple components. One component analyses statistical
                 features extracted from the raw data to be compressed
                 (seen as a sequence of 8-bit integers) to divide the
                 data into blocks. These blocks are then projected onto
                 a two-dimensional Euclidean space via two further
                 (evolved) program components. K-means clustering is
                 applied to group similar data blocks. Each cluster is
                 then labelled with the optimal compression algorithm
                 for its member blocks. Once a program that achieves
                 good compression is evolved, it can be used on unseen
                 data without the requirement for any further evolution.
                 GP-zip3 is similar to its predecessor, GP-zip2. Both
                 systems outperform a variety of standard compression
                 algorithms and are faster than other evolutionary
                 compression techniques. However, GP-zip2 was still
                 substantially slower than off-the-shelf algorithms.
                 GP-zip3 alleviates this problem by using a novel
                 fitness evaluation strategy. More specifically, GP-zip3
                 evolves and then uses decision trees to predict the
                 performance of GP individuals without requiring them to
                 be used to compress the training data. As shown in a
                 variety of experiments, this speeds up evolution in
                 GP-zip3 considerably over GP-zip2 while achieving
                 similar compression results, thereby significantly
                 broadening the scope of application of the approach.",
  DOI =          "doi:10.1109/CEC.2010.5585956",
  notes =        "WCCI 2010. Also known as \cite{5585956}",
}

Genetic Programming entries for Ahmed Kattan Riccardo Poli

Citations