Thesis (M.S.)--Brigham Young University. Dept. of Computer Science, 2006. / Includes bibliographical references (p. 47-48).

This thesis presents Genetic Algorithm for Deriving Software (Gads), a new technique for genetic programming. Gads combines a conventional genetic algorithm with a context-sensitive grammar. The key to Gads is the onto genic mapping, which converts a genome from an array of integers to a correctly typed program in the phenotype language defined by the grammar. A new type of grammar, the reflective attribute grammar (rag), is introduced. The rag is an extension of the conventional attribute grammar, which is designed to produce valid sentences, not to recognize or parse them. Together, Gads and rags provide a scalable solution for evolving type-correct software in independently-chosen context-sensitive languages. The statistics of performance comparison is investigated. A method for representing a set of genetic programming systems or problems on a cladogram is presented. A method for comparing genetic programming systems or problems on a single rational scale is proposed.

This research applies the biologically inspired, artificial evolutionary processes of Genetic Algorithms and Genetic Programming to digital hardware circuit synthesis and minimization. In this new application, three approaches are taken to genetic hardware development. First, as a method for logic synthesis, Genetic Programming is applied to the building of logic functions. Experimental results have shown the logic equations from this technique produce better than 88% coverage of the given truth-tables, but the method cannot guarantee complete (100%) coverage. Secondly, to better achieve complete function coverage, an XOR Correction Circuit Algorithm used in conjunction with the Genetic Logic Synthesis was developed. With this algorithm, the genetic logic synthesis can reiteratively attempt coverage by formulating its own selective "correction" functions, for input combinations where complete truth table coverage has not previously been achieved. With this technique, complete function coverage was synthesized in all experiments conducted. The third application of the paradigm is to the minimization of Reed-Muller Equations. In this application, a Genetic Algorithm is implemented only in the search space of all "correct", functionally equivalent equations, with only the task of finding reductions. With this limited search space the solutions have absolute guaranteed function coverage, as well as a better defined focus for the genetic evolutionary process. In both the logic synthesis and minimization processes the genetic operators determine efficient circuit implementations and reductions. The results are often different from those of human designers. Because the genetic techniques incorporate logical testing into the design and build process, one can be assured that the circuit will function as derived on completion. For all three applications, the effects of a number of evolutionary parameters on the genetic operators' problem solving capability are examined. The resulting logic and logic minimizations are also compared with both arbitrarily defined functions and well known logic synthesis benchmarks. It has been shown that genetic operators applied to digital logic can effectively find good solutions for both logic synthesis and logic minimization. / Graduation date: 1997

Thesis (M. Phil.)--University of Hong Kong, 2001. / Includes bibliographical references (leaves 71-76).

This thesis proposes two new variations on the genetic algorithm. The first attempts to improve clustering problems by optimizing the structure of a genetic string dynamically during the run of the algorithm. This is done by using a permutation on the allele which is inherited by the next generation. The second is a multiple pool technique which ensures continuing convergence by maintaining unique lineages and merging pools of similar age. These variations will be tested against two well-known graph theory problems, the Traveling Salesman Problem and the Maximum Clique Problem. The results will be analyzed with respect to string rates, child improvement, pool rating resolution, and average string age. / Department of Computer Science

Thesis (M.S.)--Rochester Institute of Technology, 2009. / Typescript. Includes bibliographical references (leaves 46-47).

Thesis (Ph.D.) - Carleton University, 2007. / Includes bibliographical references (p. 261-266). Also available in electronic format on the Internet.

Thesis (M.S.)--Worcester Polytechnic Institute. / Title from title screen. Keywords: genetic programming; approximations; machine learning; artificial intelligence. Includes bibliographical references (p. 92-94).

The school timetabling problem is a common optimization problem faced by many primary and secondary schools. Each school has its own set of requirements and constraints that are dependent on various factors such as the number of resources available and rules specified by the department of education for that country. There are two objectives in this study. In previous studies, genetic algorithms have only been used to solve a single type of school timetabling problem. The first objective of this study is to test the effectiveness of a genetic algorithm approach in solving more than one type of school timetabling problem. The second objective is to evaluate a genetic algorithm that uses an indirect representation (IGA) when solving the school timetabling problem. This IGA approach is then compared to the performance of a genetic algorithm that uses a direct representation (DGA). This approach has been covered in other domains such as job shop scheduling but has not been covered for the school timetabling problem. Both the DGA and IGA were tested on five school timetabling problems. Both the algorithms were initially developed based on findings in the literature. They were then improved iteratively based on their performance when tested on the problems. The processes of the genetic algorithms that were improved were the method of initial population creation, the selection methods and the genetic operators used. Both the DGA and the IGA were found to produce timetables that were competitive and in some cases superior to that of other methods such as simulated annealing and tabu search. It was found that different processes (i.e. the method of initial population creation, selection methods and genetic operators) were needed for each problem in order to produce the best results. When comparing the performance of the two approaches, the IGA outperformed the DGA for all of the tested school timetabling problems. / Thesis (M.Sc.)-University of KwaZulu-Natal, Pietermaritzburg, 2013.

Sin Man Cheang. / "March 2005." / Thesis (Ph.D.)--Chinese University of Hong Kong, 2005. / Includes bibliographical references (p. 219-233) / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. Ann Arbor, MI : ProQuest Information and Learning Company, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Mode of access: World Wide Web. / Abstracts in English and Chinese.