Created by W.Langdon from gp-bibliography.bib Revision:1.7325
The main motivation for this thesis was the realisation that the development of data compression algorithms capable to deal with heterogeneous data has significantly slowed down in the last few years. Furthermore, there is relatively little research on using Computational Intelligence paradigms to develop reliable universal compression systems. The primary aim of the work presented in this thesis is to make some progress towards turning the idea of using artificial evolution to evolve human-competitive general-purpose compression system into practice. We aim to improve over current compression systems by addressing their limitations in relation to heterogeneous data, particularly archive files.
Our guiding idea is to combine existing, well-known data compression schemes in order to develop an intelligent universal data compression system that can deal with different types of data effectively. The system learns when to switch from one compression algorithm to another as required by the particular regularities in a file. Genetic Programming (GP) has been used to automate this process.
This thesis contributes to the applications of GP in the lossless data compression domain. In particular we proposed a series of intelligent universal compression systems: the GP-zip family. We presented four members of this family, namely, GP-zip, GP-zip*, GP-zip2 and GP-zip3. Each new version addresses the limitations of previous systems and improves upon them. In addition, this thesis presents a new learning technique that specialised on analysing continues stream of data, detect different patterns within them and associate these patterns with different classes according to the user need. Hence, we extended this work and explored our learning technique applications to the problem of the analysing human muscles EMG signals to predict fatigue onset and the identification of file types. This thesis includes an extensive empirical evaluation of the systems developed in a variety of real world situations. Results have revealed the effectiveness of the systems.",
supervisor: Riccardo Poli",
Genetic Programming entries for Ahmed Kattan