Thesis (M.S.)--Worcester Polytechnic Institute. / Keywords: comprehensiblity; technical analysis; genetic programming; overfitting; cooperative coevolution. Includes bibliographical references (p. 82-87).

published_or_final_version / Statistics and Actuarial Science / Master / Master of Philosophy

Thesis(M.S.)--Auburn University, 2005. / Abstract. Vita. Includes bibliographic references.

Thesis (MScIng)--University of Stellenbosch, 2006. / Bibliography. Also available via the Internet.

Thesis (M.C.S.)--Carleton University, 2004. / Includes bibliographical references (p. 84-91). Also available in electronic format on the Internet.

Thesis (M.Sc.)--Victoria University of Wellington, 2008. / Includes bibliographical references.

Thesis (M.S.)--Bowling Green State University, 2009. / Document formatted into pages; contains xi, 81 p. : ill., maps. Includes bibliographical references.

Process Algebras are a Formal Modelling methodology which are an effective tool for defining models of complex systems, particularly those involving multiple interacting processes. However, describing such a model using Process Algebras requires expertise from both the modeller and the domain expert. Finding the correct model to describe a system can be difficult. Further more, even with the correct model, parameter tuning to allow model outputs to match experimental data can also be both difficult and time consuming. Evolutionary Algorithms provide effective methods for finding solutions to optimisation problems with large and noisy search spaces. Evolutionary Algorithms have been proven to be well suited to investigating parameter fitting problems in order to match known data or desired behaviour. It is proposed that Process Algebras and Evolutionary Algorithms have complementary strengths for developing models of complex systems. Evolutionary Algorithms require a precise and accurate fitness function to score and rank solutions. Process Algebras can be incorporated into the fitness function to provide this mathematical score. Presented in this work is the Evolving Process Algebra (EPA) framework, designed for the application of Evolutionary Algorithms (specifically Genetic Algorithms and Genetic Programming optimisation techniques) to models described in Process Algebra (specifically PEPA and Bio-PEPA) with the aim of evolving fitter models. The EPA framework is demonstrated using multiple complex systems. For PEPA this includes the dining philosophers resource allocation problem, the repressilator genetic circuit, the G-protein cellular signal regulators and two epidemiological problems: HIV and the measles virus. For Bio-PEPA the problems include a biochemical reactant-product system, a generic genetic network, a variant of the G-protein system and three epidemiological problems derived from the measles virus. Also presented is the EPA Utility Assistant program; a lightweight graphical user interface. This is designed to open the full functionality and parallelisation of the EPA framework to beginner or naive users. In addition, the assistant program aids in collating and graphing after experiments are completed.

Thesis (MScEng (Process Engineering))--University of Stellenbosch, 2006. / Genetic programming (GP) is a methodology that imitates genetic algorithms, which uses mutation and replication to produce algorithms or model structures based on Darwinian survival-of-the-fittest principles. Despite its obvious po-tential in process systems engineering, GP does not appear to have gained large-scale acceptance in process engineering applications. In this thesis, therefore, the following hypothesis was considered: Genetic programming offers a competitive approach towards the automatic generation of process models from data. This was done by comparing three different GP algorithms to classification and regression trees (CART) as benchmark. Although these models could be assessed on the basis of several different criteria, the assessment was limited to the predictive power and interpretability of the models. The reason for using CART as a benchmark, was that it is well-established as a nonlinear approach to modelling, and more importantly, it can generate interpretable models in the form of IF-THEN rules. Six case studies were considered. Two of these were based on simulated data (a regression and a classification problem), while the other four were based on real-world data obtained from the process industries (three classification problems and one regression problem). In the two simulated case studies, the CART models outperformed the GP models both in terms of predictive power and interpretability. In the four real word case studies, two of the GP algorithms and CART performed equally in terms of predictive power. Mixed results were obtained as far as the interpretability of the models was concerned. The CART models always produced sets of IF-THEN rules that were in principle easy to interpret. However, when many of these rules are needed to represent the system (large trees), the tree models lose their interpretability – as was indeed the case in the majority of the case studies considered. Nonetheless, the CART models produced more interpretable structures in almost all the case studies. The exception was a case study related to the classification of hot rolled steel plates (which could have surface defects or not). In this case, the one of the GP models produced a singularly simple model, with the same predictive power as that of the classification tree. Although GP models and their construction were generally more complex than classification/regression models and did not appear to afford any particular advantages in predictive power over the classification/regression trees, they could therefore provide more concise, interpretable models than CART. For this reason, the hypothesis of the thesis should arguably be accepted, especially if a high premium is placed on the development of interpretable models.

<p>Promoter detection, especially in prokaryotes, has always been an uphill task and may remain so, because of the many varieties of sigma factors employed by various organisms in transcription. The situation is made more complex by the fact, that any seemingly unimportant sequence segment may be turned into a promoter sequence by an activator or repressor (if the actual promoter sequence is made unavailable). Nevertheless, a computational approach to promoter detection has to be performed due to number of reasons. The obvious that comes to mind is the long and tedious process involved in elucidating promoters in the &lsquo / wet&rsquo / laboratories not to mention the financial aspect of such endeavors. Promoter detection/prediction of an organism with few characterized promoters (M.tuberculosis) as envisaged at the beginning of this work was never going to be easy. Even for the few known Mycobacterial promoters, most of the respective sigma factors associated with their transcription were not known. If the information (promoter-sigma) were available, the research would have been focused on categorizing the promoters according to sigma factors and training the methods on the respective categories. That is assuming that, there would be enough training data for the respective categories. Most promoter detection/prediction studies have been carried out on E.coli because of the availability of a number of experimentally characterized promoters (+- 310). Even then, no researcher to date has extended the research to the entire E.coli genome.</p>