Thesis (Ph.D.)--Tennessee Technological University, 2007. / Bibliography: leaves 49-55.

Thesis (Ed.D.)--University of Central Florida, 2008. / Adviser: Rosemarye Taylor. Includes bibliographical references (p. 166-171).

Thesis (Ph. D.)--Florida State University, 2006. / Advisor: Briley E. Proctor, Florida State University, College of Education, Dept. of Educational Psychology and Learning Systems. Title and description from dissertation home page (viewed July 28, 2006). Document formatted into pages; contains x, 122 pages. Includes bibliographical references.

Many performance-based assessment tools are available to measure upper extremity function. Most define hand function by the time taken to complete a task. A new computer game-based hand-arm function evaluation (GHA) tool has been developed to quantify fine and gross object manipulations skills. Objective of this study was to evaluate the test-retest reliability and convergent validity of the GHA assessment tool. Thirty stroke clients with the mean age of 68.41 years were recruited. Test-retest reliability was assessed by Intraclass correlation coefficient (ICCs) and t-test. Convergent validity between GHA and the Wolf Motor Function Test (WMFT) was determined using the Pearson’s correlation coefficient. Most of the GHA outcome measures had moderate to high ICC (0.5-0.9). With scant exceptions, low correlations were observed between GHA measures and WMFT score. The ICC values reflected the complexity of the tasks, more complex task showed lower ICCs values. / October 2016

Many-objective optimisation poses great challenges to evolutionary algorithms. To start with, the ineffectiveness of the Pareto dominance relation, which is the most important criterion in multi-objective optimisation, results in the underperformance of traditional Pareto-based algorithms. Also, the aggravation of the conflict between proximity and diversity, along with increasing time or space requirement as well as parameter sensitivity, has become key barriers to the design of effective many-objective optimisation algorithms. Furthermore, the infeasibility of solutions' direct observation can lead to serious difficulties in algorithms' performance investigation and comparison. In this thesis, we address these challenges, aiming to make evolutionary algorithms as effective in many-objective optimisation as in two- or three-objective optimisation. First, we significantly enhance Pareto-based algorithms to make them suitable for many-objective optimisation by placing individuals with poor proximity into crowded regions so that these individuals can have a better chance to be eliminated. Second, we propose a grid-based evolutionary algorithm which explores the potential of the grid to deal with many-objective optimisation problems. Third, we present a bi-goal evolution framework that converts many objectives of a given problem into two objectives regarding proximity and diversity, thus creating an optimisation problem in which the objectives are the goals of the search process itself. Fourth, we propose a comprehensive performance indicator to compare evolutionary algorithms in optimisation problems with various Pareto front shapes and any objective dimensionality. Finally, we construct a test problem to aid the visual investigation of evolutionary search, with its Pareto optimal solutions in a two-dimensional decision space having similar distribution to their images in a higher-dimensional objective space. The work reported in this thesis is the outcome of innovative attempts at addressing some of the most challenging problems in evolutionary many-objective optimisation. This research has not only made some of the existing approaches, such as Pareto-based or grid-based algorithms that were traditionally regarded as unsuitable, now effective for many-objective optimisation, but also pushed other important boundaries with novel ideas including bi-goal evolution, a comprehensive performance indicator and a test problem for visual investigation. All the proposed algorithms have been systematically evaluated against existing state of the arts, and some of these algorithms have already been taken up by researchers and practitioners in the field.

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Thesis (Ed.D.)--University of West Florida, 2007. / Title from title page of source document. Document formatted into pages; contains 163 pages. Includes bibliographical references.

Thesis (Ed.D.)--University of West Florida, 2007. / Title from title page of source document. Document formatted into pages; contains 111 pages. Includes bibliographical references.

Thesis (Ph.D.)--Tennessee Technological University, 2006. / Bibliography: leaves 46-53.

Thesis (Ph. D.)--Florida State University, 2006. / Advisor: Akihito Kamata, Florida State University, College of Education, Dept. of Educational Psychology and Learning Systems. Title and description from dissertation home page (viewed June 7, 2006). Document formatted into pages; contains x, 135 pages. Includes bibliographical references.