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Due to the popularity of social networking sites such as Twitter, Facebook and blogs, the amount of electronic text is continuing to grow. There is a need to categorize these vast amounts of documents and it is no surprise that the field of text categorization is a popular one. The traditional approach to text categorization is a feature-based approach, normally processing features based on words. Stream based methods have been shown to perform well in some experimentations but there has been no thorough study of their performance on a number of major corpora and their results have not been thoroughly compared against the current state-of- the-art feature based techniques. This is an important problem as the techniques cannot be fully recognized until a thorough study has been performed. The concept of protocols and how each affects categorization results has also not been studied thoroughly across a number of methods for several corpora. It is important to attempt to discover which stream based approaches perform best in which situations and how the choice of protocol affects their performance, if at all. It is hoped that it can be shown that for certain corpora or document lengths that certain approaches and protocols should be used. These findings could then drive the decision of which methods and protocols to use for future experiments. An existing problem within the field of text categorization is that it is often difficult to recreate the exact experimentation conditions of previous studies. One reason for this is that the training and testing splits often differ and it was important that this study did not add to this existing problem, that the experimentations could be accurately recreated and that others would be fairly compared. A toolkit has been developed that allows all of the methods and protocols to be compared in a consistent manner. The toolkit models the streams using suffix trees and all of the stream based methods have been implemented. In addition to the implementation of existing techniques, a number of new stream based methods have been detailed within the thesis and one of these new techniques, R-Ranges, has been shown to outperform all other methods for two of the corpora, including PPM (Prediction by Partial Matching) variants, state-of-the-art techniques that are mathematically well supported. The experimentation has also shown that the protocol (whether static or dynamic training models are used in addition to training documents of the same category being concatenated or not) does indeed affect the accuracies of each method. The concatenated dynamic protocol was found to outperform all others and performs consistently well across all methods, for all corpora. This study has now conclusively shown that the method used to categorize text must not be the only one, the selection of protocol is also just as important.

Over the past few decades, meta-heuristic algorithms (MHs) have proven to be powerful tools for dealing with difficult combinational optimization problems (COPs). These techniques can obtain high quality solutions within reasonable computational time for many hard ,.' A' problems. Among these methods, guided local search (GLS) is p'femising one. The proximate optimality principle (POP), an underlying assumption in most meta-heuristics, assumes that good solutions have similar structures. Structures which are common to good solutions are more likely to be part of the best solution. In this thesis we discuss how the performance of the GLS can be further enhanced through designing a cooperative mechanism based on the proximate optimality principle (POP). The approach that we took was to search for solutions using multiple agents, each of which running a copy of GLS. These agents benefit from each other through the exchange of information based on POP. We suggest based on POP that common features that appear in many locally optimal solutions of GLS agents are more likely to be parts of the globally optimal solution. Thus, this property should be taken into consideration during the search. We call this framework Population-based GLS (P-GLS). P-GLS shows its efficiency and effectiveness to converge quickly to promising regions of the search space in an intelligent manner. Four P-GLS versions are proposed which enhance the performance of P-GLS. These algorithms are extensively studied and tested on Traveling salesman problem (TSP), Multidimensional Knapsack Problem (MKP) and Field Workforce Scheduling Problem (FWSP). Computational results confirm the effectiveness of P-GLS compared to original GLS and other well known MHs.