Evolutionary Lossless Compression with GP-ZIP
Created by W.Langdon from
gp-bibliography.bib Revision:1.7964
- @InProceedings{kattan08:_gp_zip,
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author = "Ahmad Kattan and Riccardo Poli",
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title = "Evolutionary Lossless Compression with {GP-ZIP}",
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booktitle = "2008 IEEE World Congress on Computational
Intelligence",
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year = "2008",
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editor = "Jun Wang",
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pages = "2468--2472",
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address = "Hong Kong",
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month = "1-6 " # jun,
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organization = "IEEE Computational Intelligence Society",
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publisher = "IEEE Press",
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keywords = "genetic algorithms, genetic programming",
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isbn13 = "978-1-4244-1823-7",
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file = "EC0569.pdf",
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DOI = "doi:10.1109/CEC.2008.4631128",
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abstract = "In this paper we propose a new approach for applying
Genetic Programming to loss-less data compression based
on combining well-known lossless compression
algorithms. The file to be compressed is divided into
chunks of a predefined length, and GP is asked to find
the best possible compression algorithm for each chunk
in such a way to minimise the total length of the
compressed file. This technique is referred to as
''GP-zip''. The compression algorithms available to
GP-zip (its function set) are: Arithmetic coding (AC),
Lempel-Ziv-Welch (LZW), Unbounded Prediction by Partial
Matching (PPMD), Run Length Encoding (RLE), and Boolean
Minimisation. In addition, two transformation
techniques are available: Burrows-Wheeler
Transformation (BWT) and Move to Front (MTF). In
experimentation with this technique, we show that when
the file to be compressed is composed of heterogeneous
data fragments (as is the case, for example, in archive
files), GP-zip is capable of achieving compression
ratios that are superior to those obtained with
well-known compression algorithms.",
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notes = "WCCI 2008 - A joint meeting of the IEEE, the INNS, the
EPS and the IET.",
- }
Genetic Programming entries for
Ahmed Kattan
Riccardo Poli
Citations