In a democratic society, the number of public opinion documents increase with days, and there is a pressing need for automatically classifying these documents. Traditional approach for classifying documents involves the techniques of segmenting words and the use of stop words, corpus, and grammar analysis for retrieving the key terms of documents. However, with the emergence of new terms, the traditional methods that leverage dictionary or thesaurus may incur lower accuracy. Therefore, this study proposes a new method that does not require the prior establishment of a dictionary or thesaurus, and is applicable to documents written in any language and documents containing unstructured text. Specifically, the classification method employs genetic algorithm for achieving this goal. In this method, each training document is represented by several chromosomes, and based on the gene values of these chromosomes, the characteristic terms of the document are determined. The fitness function, which is required by the genetic algorithm for evaluating the fitness of an evolved chromosome, considers the similarity to the chromosomes of documents of other types. This study used data FAQ of e-mail box of Taipei city mayor for evaluating the proposed method by varying the length of documents. The results show that the proposed method achieves the average accuracy rate of 89%, the average precision rate of 47%, and the average recall rate of 45%. In addition, F-measure can reach up to 0.7. The results confirms that the number of training documents, content of training documents, the similarity between the types of documents, and the length of the documents all contribute to the effectiveness of the proposed method.

Feature selection is an important pre-processing step in mining and learning. A good set of features can not only improve the accuracy of classification, but also reduce the time to derive rules. It is executed especially when the amount of attributes in a given training data is very large. This thesis thus proposes three GA-based clustering methods for attribute clustering and feature selection. In the first method, each feasible clustering result is encoded into a chromosome with positive integers and a gene in the chromosome is for an attribute. The value of a gene represents the cluster to which the attribute belongs. The fitness of each individual is evaluated using both the average accuracy of attribute substitutions in clusters and the cluster balance. The second method further extends the first method to improve the time performance. A new fitness function based on both the accuracy and the attribute dependency is proposed. It can reduce the time of scanning the data base. The third approach uses another encoding method for representing chromosomes. It can achieve a faster convergence and a better result than the second one. At last, the experimental comparison with the k-means clustering approach and with all combinations of attributes also shows the proposed approach can get a good trade-off between accuracy and time complexity. Besides, after feature selection, the rules derived from only the selected features may usually be hard to use if some values of the selected features cannot be obtained in current environments. This problem can be easily solved in our proposed approaches. The attributes with missing values can be replaced by other attributes in the same clusters. The proposed approaches thus provide flexible alternatives for feature selection.

<p>Artificial intelligence plays an increasingly important role in modern computer games. As the complexity of the games increase, so does the complexity of the AI.</p><p>The aim of this dissertation is to investigate how AI for a turnbased computer game can coevolve into playing smarter by combining genetic algorithms with neural networks and using a reinforcement learning regime.</p><p>The results have shown that a coevolved AI can reach a high performance in this kind of turnbased strategy games. It also shows that how the data is coded and decoded and which strategy that is used plays a very big role in the final results</p>

Thesis (Ph. D.)--University of Missouri-Columbia, 1999. / Typescript. Vita. Includes bibliographical references (leaves 196-200). Also available on the Internet.

Thesis (Ph. D.)--University of Missouri-Columbia, 1999. / Typescript. Vita. Includes bibliographical references (leaves 196-200). Also available on the Internet.

Thesis (M. Phil.)--University of Hong Kong, 2001. / Includes bibliographical references (leaves 139-145).

Thesis (M.S.)--Brigham Young University. Dept. of Civil and Environmental Engineering, 2004. / Includes bibliographical references (p. 87-90).

Thesis (Ph.D.)--University of Central Florida, 2009. / Adviser: Avelino Gonzalez. Includes bibliographical references (p. 275-288).

Thesis (M.S.)--University of West Florida, 2009. / Submitted to the Dept. of Computer Science. Title from title page of source document. Document formatted into pages; contains 66 pages. Includes bibliographical references.

Thesis (M.S. in Physics)--Naval Postgraduate School, June 2003. / Thesis advisor(s): Michael Melich, David Jenn, Rodney Johnson. Includes bibliographical references (p. 113-115). Also available online.