Evolutionary Lossless Compression with GP-ZIP

Created by W.Langdon from gp-bibliography.bib Revision:1.8633

@InProceedings{kattan08:_gp_zip,

• author = "Ahmad Kattan and Riccardo Poli",
• title = "Evolutionary Lossless Compression with {GP-ZIP}",
• booktitle = "2008 IEEE World Congress on Computational Intelligence",
• year = "2008",
• editor = "Jun Wang",
• pages = "2468--2472",
• address = "Hong Kong",
• month = "1-6 " # jun,
• organization = "IEEE Computational Intelligence Society",
• publisher = "IEEE Press",
• keywords = "genetic algorithms, genetic programming, GPzip, Compression algorithms, Evolutionary computation, Computers, Data compression",
• isbn13 = "978-1-4244-1823-7",
• file = "EC0569.pdf",
• DOI = "10.1109/CEC.2008.4631128",
• abstract = "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.",
• 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