Lau, Wai Shing. / Thesis submitted in: November 2006. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2007. / Includes bibliographical references (leaves 85-94). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgement --- p.iv / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Field Programmable Gate Arrays --- p.2 / Chapter 1.2 --- FPGA technology mapping problem --- p.3 / Chapter 1.3 --- Motivations --- p.5 / Chapter 1.4 --- Contributions --- p.6 / Chapter 1.5 --- Thesis Organization --- p.9 / Chapter 2 --- Background Study --- p.11 / Chapter 2.1 --- Deterministic approach to technology mapping problem --- p.11 / Chapter 2.1.1 --- FlowMap --- p.12 / Chapter 2.1.2 --- DAOMap --- p.14 / Chapter 2.2 --- Stochastic approach --- p.15 / Chapter 2.2.1 --- Bio-Inspired Methods for Multi-Level Combinational Logic Circuit Design --- p.15 / Chapter 2.2.2 --- A Survey of Combinational Logic Circuit Representations in stochastic algorithms --- p.17 / Chapter 2.3 --- Genetic Parallel Programming --- p.20 / Chapter 2.3.1 --- Accelerating Phenomenon --- p.22 / Chapter 2.4 --- Chapter Summary --- p.23 / Chapter 3 --- A GPP based Logic Circuit Synthesizer --- p.24 / Chapter 3.1 --- Overall system architecture --- p.25 / Chapter 3.2 --- Multi-Logic-Unit Processor --- p.26 / Chapter 3.3 --- The Genotype of a MLP program --- p.28 / Chapter 3.4 --- The Phenotype of a MLP program --- p.31 / Chapter 3.5 --- The Evolution Engine --- p.33 / Chapter 3.5.1 --- The Dual-Phase Approach --- p.33 / Chapter 3.5.2 --- Genetic operators --- p.35 / Chapter 3.6 --- Chapter Summary --- p.38 / Chapter 4 --- MLP in hardware --- p.39 / Chapter 4.1 --- Motivation --- p.39 / Chapter 4.2 --- Hardware Design and Implementation --- p.40 / Chapter 4.3 --- Experimental Settings --- p.43 / Chapter 4.4 --- Experimental Results and Evaluations --- p.46 / Chapter 4.5 --- Chapter Summary --- p.50 / Chapter 5 --- Feasibility Study of Multi MLPs --- p.51 / Chapter 5.1 --- Motivation --- p.52 / Chapter 5.2 --- Overall Architecture --- p.53 / Chapter 5.3 --- Experimental settings --- p.55 / Chapter 5.4 --- Experimental results and evaluations --- p.59 / Chapter 5.5 --- Chapter Summary --- p.59 / Chapter 6 --- A Hybridized GPPLCS --- p.61 / Chapter 6.1 --- Motivation --- p.62 / Chapter 6.2 --- Overall system architecture --- p.62 / Chapter 6.3 --- Experimental settings --- p.64 / Chapter 6.4 --- Experimental results and evaluations --- p.66 / Chapter 6.5 --- Chapter Summary --- p.70 / Chapter 7 --- A Memetic GPPLCS --- p.71 / Chapter 7.1 --- Motivation --- p.72 / Chapter 7.2 --- Overall system architecture --- p.72 / Chapter 7.3 --- Experimental settings --- p.76 / Chapter 7.4 --- Experimental results and evaluations --- p.77 / Chapter 7.5 --- Chapter Summary --- p.80 / Chapter 8 --- Conclusion --- p.82 / Chapter 8.1 --- Future work --- p.83 / Bibliography --- p.85

This thesis describes the use of genetic programming to automate the discovery of numerical approximation formulae. Results are presented involving rediscovery of known approximations for Harmonic numbers and discovery of rational polynomial approximations for functions of one or more variables, the latter of which are compared to Padé approximations obtained through a symbolic mathematics package. For functions of a single variable, it is shown that evolved solutions can be considered superior to Padé approximations, which represent a powerful technique from numerical analysis, given certain tradeoffs between approximation cost and accuracy, while for functions of more than one variable, we are able to evolve rational polynomial approximations where no Padé approximation can be computed. Furthermore, it is shown that evolved approximations can be iteratively improved through the evolution of approximations to their error function. Based on these results, we consider genetic programming to be a powerful and effective technique for the automated discovery of numerical approximation formulae.

Thesis (Ph. D.)--Missouri University of Science and Technology, 2009. / Vita. The entire thesis text is included in file. Title from title screen of thesis/dissertation PDF file (viewed January 5, 2010) Includes bibliographical references (p. 152-159).

The entire thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file; a non-technical public abstract appears in the public.pdf file. Title from PDF of title page (University of Missouri--Columbia, viewed on January 19, 2010). Thesis advisor: Dr. Tina Smilkstein. Includes bibliographical references.

Intelligent programming tutors have proven to be an economically viable and effective means of assisting novice programmers overcome learning difficulties. However, the large-scale use of intelligent programming tutors has been impeded by the high developmental costs associated with building intelligent programming tutors. The research presented in this thesis forms part of a larger initiative aimed at reducing these costs by building a generic architecture for the development of intelligent programming tutors. One of the facilities that must be provided by the generic architecture is the automatic generation of solutions to programming problems. The study presented in the thesis examines the use of genetic programming as means of inducing solution algorithms to novice programming problems. The scope of the thesis is limited to novice procedural programming paradigm problems requiring the use of arithmetic, string manipulation, conditional, iterative and recursive programming structures. The methodology employed in the study is proof-by-demonstration. A genetic programming system for the induction of novice procedural solution algorithms was implemented and tested on randomly chosen novice procedural programming problems. The study has identified the standard and advanced genetic programming features needed for the successful generation of novice procedural solution algorithms. The outcomes of this study include the derivation of an internal representation language for representing procedural solution algorithms and a high-level programming problem specification format for describing procedural problems, in the generic architecture. One of the limitations of genetic programming is its susceptibility to converge prematurely to local optima and not find a solution in some cases. The study has identified fitness function biases against certain structural components that are needed to find a solution, as an additional cause of premature convergence in this domain. It presents an iterative structure-based algorithm as a solution to this problem. This thesis has contributed to both the fields of genetic programming and intelligent programming tutors. While genetic programming has been successfully implemented in various domains, it is usually applied to a single problem within that domain. In this study the genetic programming system must be capable of solving a number of different programming problems in different application domains. In addition to this, the study has also identified a means of overcoming premature convergence caused by fitness function biases in a genetic programming system for the induction of novice procedural programming algorithms. Furthermore, although a number of studies have addressed the student modelling and pedagogical aspects of intelligent programming tutors, none have examined the automatic generation of problem solutions as a means of reducing developmental costs. Finally, this study has contributed to the ongoing research being conducted by the artificial intelligence in education community, to test the effectiveness of using machine learning techniques in the development of different aspects of intelligent tutoring systems. / Thesis (Ph.D.)-University of KwaZulu-Natal, 2004.

<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>

Thesis (Ph.D.)--Auburn University, 2007. / Abstract. Includes bibliographic references (ℓ. 167-176)

Thesis (Ph.D.)--University of Adelaide, Dept. of Applied Mathematics, 1998. / Bibliography: leaves 179-190.

Thesis (Ph. D.)--Electrical and Computer Engineering, Georgia Institute of Technology, 2007. / Committee Chair: Vachtsevanos, George J.; Committee Co-Chair: Litt, Brian; Committee Member: Butera, Robert J.; Committee Member: Echauz, Javier; Committee Member: Howard, Ayanna M.; Committee Member: Williams, Douglas B.

Thesis (M.C.S.) - Carleton University, 2002. / Includes bibliographical references (p. 139-150). Also available in electronic format on the Internet.