Thesis (M. Phil.)--University of Hong Kong, 1989.

published_or_final_version / Statistics / Master / Master of Philosophy

Thesis (M.S.)--Virginia Polytechnic Institute and State University, 1990. / Vita. Abstract. Includes bibliographical references (leaves 60-61). Also available via the Internet.

Thesis (Ph. D.)--University of Washington, 1998. / Vita. Includes bibliographical references (leaves [191]-194).

Thesis (Ph. D.)--University of Wisconsin--Madison, 1986. / Vita. eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references.

[en] This work consists in three parts: the first experimental, the second analytical and the third analytical-computational. In the first part is found experimentally, the transfer function of an electrodynamic multivibrator, is obtained experimentally by frequencial analysis. The second part presents the development of analytical multivibrator model and from it the analytical transfer function is obtained. Finally, in the third part the author obtains an approximate value of the model’s parameters which are introduced in an optimization’s subroutine to make the parameters. / [es] Este trabajo consta de tres partes: la primera es experimental, la segunda analítica y la tercera es analítica y computacional. En la primera parte es encontrada experimentalmente la función de transferencia de un multivibrador electrodinámico, la cual es obtenida experimentalmente por análisis frecuencial. La segunda parte presenta el desarrollo analítico del modelo del multivibrador y a partir de este modelo es obtenida la función de transferencia analítica. Finalmente, en la tercera parte el autor obtiene un valor aproximado de los parámetros del modelo, los cuales son introducidos en la rutina de optimización que busca mejorar estos parámetros.

Many industrial problems in various fields, such as logistics, process management, orproduct design, can be formalized and expressed as optimization problems in order tomake them solvable by optimization algorithms. However, solvers that guarantee thefinding of optimal solutions (complete) can in practice be unacceptably slow. Thisis one of the reasons why approximative (incomplete) algorithms, producing near-optimal solutions under restrictions (most dominant time), are of vital importance. Those approximative algorithms go under the umbrella term metaheuristics, each of which is more or less suitable for particular optimization problems. These algorithmsare flexible solvers that only require a representation for solutions and an evaluation function when searching the solution space for optimality.What all metaheuristics have in common is that their search is guided by certain control parameters. These parameters have to be manually set by the user andare generally problem and interdependent: A setting producing near-optimal resultsfor one problem is likely to perform worse for another. Automating the parameter setting process in a sophisticated, computationally cheap, and statistically reliable way is challenging and a significant amount of attention in the artificial intelligence and operational research communities. This activity has not yet produced any major breakthroughs concerning the utilization of problem instance knowledge or the employment of dynamic algorithm configuration. The thesis promotes automated parameter optimization with reference to the inverse impact of problem instance diversity on the quality of parameter settings with respect to instance-algorithm pairs. It further emphasizes the similarities between static and dynamic algorithm configuration and related problems in order to show how they relate to each other. It further proposes two frameworks for instance-based algorithm configuration and evaluates the experimental results. The first is a recommender system for static configurations, combining experimental design and machine learning. The second framework can be used for static or dynamic configuration,taking advantage of the iterative nature of population-based algorithms, which is a very important sub-class of metaheuristics. A straightforward implementation of framework one did not result in the expected improvements, supposedly because of pre-stabilization issues. The second approach shows competitive results in the scenario when compared to a state-of-the-art model-free configurator, reducing the training time by in excess of two orders of magnitude.

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by Wai-yin Poon. / Bibliography: leaves 62-64 / Thesis (M.Ph.)--Chinese University of Hong Kong, 1985