The idea of automatic programming using the genetic programming paradigm is a concept that has been explored in the work of Koza and several works since. Most problems attempted using genetic programming are finite in size, meaning that the problem involved evolving a function that operates over a finite domain, or evolving a routine that will only run for a finite amount of time. For problems with a finite domain, the internal representation of each individual is typically a finite automaton that is unable to store an unbounded amount of data. This thesis will address the problem of applying genetic programming to problems that have a ``discrete unbounded domain", meaning the problem involves evolving a function that operates over an unbounded domain with discrete quantities. For problems with an discrete unbounded domain, the range of possible behaviors achievable by the evolved functions increases with more versatile internal memory schemes for each of the individuals. The specific problem that I will address in this thesis is the problem of evolving a real-time deciding program for a fixed language of strings. I will discuss two paradigms that I will use to attempt this problem. Each of the paradigms will allow each individual to store an unbounded amount of data, using an internal memory scheme with at least the capabilities of a Turing tape. As each character of an input string is being processed in real time, the individual will be able to imitate a single step of a Turing machine. While the real-time restriction will certainly limit the languages for which a decider may be evolved, the fact that the evolved deciding programs run in real-time yields possible applications for these paradigms in the discovery of new algorithms. The first paradigm that I will explore will take a naive approach that will ultimately prove to be unsuccessful. The second paradigm that I will explore will take a more careful approach that will have a much greater success, and will provide insight into the design of genetic programming paradigms for problems over a discrete unbounded domain.

本文以2003至2009年的資料為研究區間，採用基本面分析指標、技術面分析指標及基因規畫法對倫敦黃金午後定盤價每季帄均塑造金價預測模型，同時歸納以基因規畫法塑造金價預測模型時，應使用何種投入指標與相關基因規畫法參數設定，較有機會獲得較佳預測力的金價預測模型。 最後發現對於黃金價格而言，各國股市大盤及黃金供需相關因素為使用基因規畫法塑造金價預測模型時較重要的指標種類，而於經濟狀況有劇烈變動時，加入技術分析指標將會改善模型的表現。而比較指標與基因規畫設定參數(如挑選函式、運算子集合、演化代數、染色體群大小)對模型預測力之影響，發現指標對模型預測力的影響遠大於基因規畫設定參數。 / The research uses the data between 2003 to 2009 to discuss the gold price forecastting model. Using fundamental analysis indices, technical analysis indices and Genetic Programming(GP) to modeling the gold price forecastting model. This paper also summarized that what kind of indexes and GP parameters should be set for getting better performance? Finally found that ,using the stock indices of important market and gold supply/demand factors to modeling usually get better performance. If there are drastic changes in economic conditions, using the technical analysis indices can improve the performance of model. The comparison of influence on model performance between indexes and GP parameters(ex. selecetio function, operator set, reproducting times, population size) show that, the indices have more influence to model performance than GP parameters.

<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>

Evolvable Hardware is a technique derived from evolutionary computation applied to a hardware design. The term evolutionary computation involves similar steps as involved in the human evolution. It has been given names in accordance with the electronic technology like, Genetic Algorithm (GA), Evolutionary Strategy (ES) and Genetic Programming (GP). In evolutionary computing, a configured bit is considered as a human chromosome for a genetic algorithm, which has to be downloaded into hardware. Early evolvable hardware experiments were conducted in simulation and the only elite chromosome was downloaded to the hardware, which was labelled as Extrinsic Hardware. With the invent of Field Programmable Gate Arrays (FPGAs) and Reconfigurable Processing Units (RPUs), it is now possible for the implementation solutions to be fast enough to evaluate a real hardware circuit within an evolutionary computation framework; this is called an Intrinsic Evolvable Hardware. This research has been taken in continuation with project 'Evolvable Hardware' done at Manukau Institute of Technology (MIT). The project was able to manually evolve two simple electronic circuits of NAND and NOR gates in simulation. In relation to the project done at MIT this research focuses on the following: To automate the simulation by using In Circuit Debugging Emulators (IDEs), and to develop a strategy of configuring hardware like an FPGA without the use of their company supplied in circuit debugging emulators, so that the evolution of an intrinsic evolvable hardware could be controlled, and is hardware independent. As mentioned, the research conducted here was able to develop an evolvable hardware friendly Generic Structure which could be used for the development of evolvable hardware. The structure developed was hardware independent and was able to run on various FPGA hardware’s for the purpose of intrinsic evolution. The structure developed used few configuration bits as compared to current evolvable hardware designs.

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.

Thesis (M.S.)--State University of New York at Binghamton, Thomas J. Watson School of Engineering and Applied Science, Department of Electrical and Computer Engineering, 2008. / Includes bibliographical references.

Thesis (Ph.D.)--University of North Carolina at Charlotte, 2005. / Includes bibliographical references (leaves 71-79).