Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Arithmetic coding for data compression
Communications of the ACM
Automatic synthesis of compression techniques for heterogeneous files
Software—Practice & Experience
Evolving Compression Preprocessors With Genetic Programming
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
A Corpus for the Evaluation of Lossless Compression Algorithms
DCC '97 Proceedings of the Conference on Data Compression
Move-to-Front and Inversion Coding
DCC '00 Proceedings of the Conference on Data Compression
Unbounded length contexts for PPM
DCC '95 Proceedings of the Conference on Data Compression
New Research on Scalability of Lossless Image Compression by GP Engine
EH '05 Proceedings of the 2005 NASA/DoD Conference on Evolvable Hardware
Fundamental Data Compression
Evolutionary lossless compression with GP-ZIP*
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Programmatic compression of images and sound
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
Evolutionary lossless compression with GP-ZIP*
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Genetic-programming based prediction of data compression saving
EA'09 Proceedings of the 9th international conference on Artificial evolution
Evolution of human-competitive lossless compression algorithms with GP-zip2
Genetic Programming and Evolvable Machines
A simple adaptive algorithm for numerical optimization
MICAI'12 Proceedings of the 11th Mexican international conference on Advances in Computational Intelligence - Volume Part II
| Hi-index | 0.00 |
In recent research we proposed GP-zip, a system which uses evolution to find optimal ways to combine standard compression algorithms for the purpose of maximally losslessly compressing files and archives. The system divides files into blocks of predefined length. It then uses a linear, fixed-length representation where each primitive indicates what compression algorithm to use for a specific data block. GP-zip worked well with heterogonous data sets, providing significant improvements in compression ratio compared to some of the best standard compression algorithms. In this paper we propose a substantial improvement, called GP-zip*, which uses a new representation and intelligent crossover and mutation operators such that blocks of different sizes can be evolved. Like GP-zip, GP-zip* finds what the best compression technique to use for each block is. The compression algorithms available in the primitive set of GP-zip* are: Arithmetic coding (AC), Lempel-Ziv-Welch (LZW), Unbounded Prediction by Partial Matching (PPMD), Run Length Encoding (RLE), and Boolean Minimization. In addition, two transformation techniques are available: the Burrows-Wheeler Transformation (BWT) and Move to Front (MTF). Results show that GP-zip* provides improvements in compression ratio ranging from a fraction to several tens of percent over its predecessor.