Page 144 - 49A Field Guide to Genetic Programming

130 12 Applications

with standard compression methods, the time needed for compression being
measured in hours or even days. Acceleration in GP image compression was
achieved in (He, Wang, Zhang, Wang, and Fang, 2005), where an optimal
linear predictive technique was proposed which used a less complex fitness
function.
Recently Kattan and Poli (2008) proposed a GP system called GP-ZIP
for lossless data compression based on the idea of optimally combining well-
known lossless compression algorithms. The file to be compressed was di-
vided into chunks of a predefined length, and GP was asked to find the best
possible compression algorithm for each chunk in such a way as to minimise
the total length of the compressed file. The compression algorithms avail-
able to GP-ZIP included arithmetic coding, Lempel-Ziv-Welch, unbounded
prediction by partial matching, and run length encoding among others. Ex-
perimentation showed that when the file to be compressed is composed of
heterogeneous data fragments (as it is the case, for example, in archive files),
GP-zip is capable of achieving compression ratios that are significantly su-
perior to those obtained with other compression algorithms.