Thesis (Ph. D.)--Auburn University. / Abstract. Vita. Includes bibliographical references (p. 97-101).

Thesis (M.S.)--Virginia Polytechnic Institute and State University, 1991. / Vita. Abstract. Includes bibliographical references (leaves 194-195). Also available via the Internet.

Thesis (M.A.)--University of Louisville, 2004. / Department of History. Vita. "May 2004." Includes bibliographical references (leaves 84-99).

Thesis (Ph. D.)--Auburn University, 1998. / Advisers: John D. Jackson, Robert B. Ekelund, Jr. Includes bibliographical references.

Lau Siu Ping. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2001. / Includes bibliographical references (leaf 44). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Pari-Mutuel System --- p.1 / Chapter 1.2 --- Different Types of Betting --- p.4 / Chapter 1.3 --- Overview --- p.6 / Chapter 2 --- Testing on Tipsters Prediction --- p.8 / Chapter 2.1 --- Introduction --- p.8 / Chapter 2.2 --- Summary Tables on Tipsters Performance --- p.11 / Chapter 2.3 --- Tipsters Prediction Vs Random Betting --- p.15 / Chapter 3 --- Multinomial Logistic Regression --- p.19 / Chapter 3.1 --- Review --- p.19 / Chapter 3.2 --- Proposed Models for the Horse Racing --- p.23 / Chapter 3.3 --- Simulation and Result --- p.26 / Chapter 3.4 --- Comparison between four Models --- p.35 / Chapter 3.5 --- Concluding Remarks --- p.36 / Appendix I --- p.37 / Reference --- p.44

Thesis (Ph. D.)--Georgia Institute of Technology, 2004. / UMI number: 3199305. Includes bibliographical references (leaves 278-289). Also available online (World Wide Web)

Xiong, Liying. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2010. / Includes bibliographical references (leave 34). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 2 --- Hong Kong Horse Racing Market and Models in Horse Racing --- p.3 / Chapter 2.1 --- Hong Kong Horse Racing Market --- p.4 / Chapter 2.2 --- Models in Horse Racing --- p.5 / Chapter 3 --- Probit Regression Model Incorporating with Public Estimates --- p.9 / Chapter 3.1 --- Estimation under No Particular Conditions --- p.10 / Chapter 3.2 --- Estimators under Particular Condition --- p.15 / Chapter 4 --- Prediction and Testing --- p.23 / Chapter 4.1 --- Prediction of Win Probability --- p.24 / Chapter 5 --- Conclusion --- p.32 / Bibliography --- p.34

<p>A metaheuristic is a generic algorithmic template that, once properly instantiated, can be used for finding high quality solutions of combinatorial optimization problems. For obtaining a fully functioning algorithm, a metaheuristic needs to be configured: typically some modules need to be instantiated and some parameters need to be tuned. For the sake of precision, we use the expression <em>parametric tuning</em> for referring to the tuning of numerical parameters, either continuous or discrete but in any case ordinal. On the other hand, we use the expression <em>structural tuning</em> for referring to the problem of defining which modules should be included and, in general, to the problem of tuning parameters that are either boolean or categorical. Finally, with <em>tuning</em> we refer to the composite <em>structural and parametric tuning</em>.</p> <p>Tuning metaheuristics is a very sensitive issue both in practical applications and in academic studies. Nevertheless, a precise definition of the tuning problem is missing in the literature. In this thesis, we argue that the problem of tuning a metaheuristic can be profitably described and solved as a machine learning problem.</p> <p>Indeed, looking at the problem of tuning metaheuristics from a machine learning perspective, we are in the position of giving a formal statement of the tuning problem and to propose an algorithm, called F-Race, for tackling the problem itself. Moreover, always from this standpoint, we are able to highlight and discuss some catches and faults in the current research methodology in the metaheuristics field, and to propose some guidelines.</p> <p>The thesis contains experimental results on the use of F-Race and some examples of practical applications. Among others, we present a feasibility study carried out by the German-based software company <em>SAP</em>, that concerned the possible use of F-Race for tuning a commercial computer program for vehicle routing and scheduling problems. Moreover, we discuss the successful use of F-Race for tuning the best performing algorithm submitted to the <em>International Timetabling Competition</em> organized in 2003 by the <em>Metaheuristics Network</em> and sponsored by <em>PATAT</em>, the international series of conferences on the <em>Practice and Theory of Automated Timetabling</em>.</p>

In this thesis we used genetic algorithms as a core gameplay mechanic for games. We created a flexible genetic algorithms framework that allowed us to iterate quickly through various designs and prototypes of games. We developed two iterations of fighting robots game and a racing game that used our framework to implement genetic algorithms. Playtesting showed that such a sophisticated game mechanic like this one can be fun and appealing to players.

In this thesis we used genetic algorithms as a core gameplay mechanic for games. We created a flexible genetic algorithms framework that allowed us to iterate quickly through various designs and prototypes of games. We developed two iterations of fighting robots game and a racing game that used our framework to implement genetic algorithms. Playtesting showed that such a sophisticated game mechanic like this one can be fun and appealing to players.