by Ngan Po Shun. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1998. / Includes bibliographical references (leaves 109-115). / Abstract also in Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Data Mining --- p.1 / Chapter 1.2 --- Motivation --- p.4 / Chapter 1.3 --- Contributions of the research --- p.5 / Chapter 1.4 --- Organization of the thesis --- p.6 / Chapter 2 --- Related Work in Data Mining --- p.9 / Chapter 2.1 --- Decision Tree Approach --- p.9 / Chapter 2.1.1 --- ID3 --- p.10 / Chapter 2.1.2 --- C4.5 --- p.11 / Chapter 2.2 --- Classification Rule Learning --- p.13 / Chapter 2.2.1 --- AQ algorithm --- p.13 / Chapter 2.2.2 --- CN2 --- p.14 / Chapter 2.2.3 --- C4.5RULES --- p.16 / Chapter 2.3 --- Association Rule Mining --- p.16 / Chapter 2.3.1 --- Apriori --- p.17 / Chapter 2.3.2 --- Quantitative Association Rule Mining --- p.18 / Chapter 2.4 --- Statistical Approach --- p.19 / Chapter 2.4.1 --- Chi Square Test and Bayesian Classifier --- p.19 / Chapter 2.4.2 --- FORTY-NINER --- p.21 / Chapter 2.4.3 --- EXPLORA --- p.22 / Chapter 2.5 --- Bayesian Network Learning --- p.23 / Chapter 2.5.1 --- Learning Bayesian Networks using the Minimum Descrip- tion Length (MDL) Principle --- p.24 / Chapter 2.5.2 --- Discretizating Continuous Attributes while Learning Bayesian Networks --- p.26 / Chapter 3 --- Overview of Evolutionary Computation --- p.29 / Chapter 3.1 --- Evolutionary Computation --- p.29 / Chapter 3.1.1 --- Genetic Algorithm --- p.30 / Chapter 3.1.2 --- Genetic Programming --- p.32 / Chapter 3.1.3 --- Evolutionary Programming --- p.34 / Chapter 3.1.4 --- Evolution Strategy --- p.37 / Chapter 3.1.5 --- Selection Methods --- p.38 / Chapter 3.2 --- Generic Genetic Programming --- p.39 / Chapter 3.3 --- Data mining using Evolutionary Computation --- p.43 / Chapter 4 --- Applying Generic Genetic Programming for Rule Learning --- p.45 / Chapter 4.1 --- Grammar --- p.46 / Chapter 4.2 --- Population Creation --- p.49 / Chapter 4.3 --- Genetic Operators --- p.50 / Chapter 4.4 --- Evaluation of Rules --- p.52 / Chapter 5 --- Learning Multiple Rules from Data --- p.56 / Chapter 5.1 --- Previous approaches --- p.57 / Chapter 5.1.1 --- Preselection --- p.57 / Chapter 5.1.2 --- Crowding --- p.57 / Chapter 5.1.3 --- Deterministic Crowding --- p.58 / Chapter 5.1.4 --- Fitness sharing --- p.58 / Chapter 5.2 --- Token Competition --- p.59 / Chapter 5.3 --- The Complete Rule Learning Approach --- p.61 / Chapter 5.4 --- Experiments with Machine Learning Databases --- p.64 / Chapter 5.4.1 --- Experimental results on the Iris Plant Database --- p.65 / Chapter 5.4.2 --- Experimental results on the Monk Database --- p.67 / Chapter 6 --- Bayesian Network Learning --- p.72 / Chapter 6.1 --- The MDLEP Learning Approach --- p.73 / Chapter 6.2 --- Learning of Discretization Policy by Genetic Algorithm --- p.74 / Chapter 6.2.1 --- Individual Representation --- p.76 / Chapter 6.2.2 --- Genetic Operators --- p.78 / Chapter 6.3 --- Experimental Results --- p.79 / Chapter 6.3.1 --- Experiment 1 --- p.80 / Chapter 6.3.2 --- Experiment 2 --- p.82 / Chapter 6.3.3 --- Experiment 3 --- p.83 / Chapter 6.3.4 --- Comparison between the GA approach and the greedy ap- proach --- p.91 / Chapter 7 --- Medical Data Mining System --- p.93 / Chapter 7.1 --- A Case Study on the Fracture Database --- p.95 / Chapter 7.1.1 --- Results of Causality and Structure Analysis --- p.95 / Chapter 7.1.2 --- Results of Rule Learning --- p.97 / Chapter 7.2 --- A Case Study on the Scoliosis Database --- p.100 / Chapter 7.2.1 --- Results of Causality and Structure Analysis --- p.100 / Chapter 7.2.2 --- Results of Rule Learning --- p.102 / Chapter 8 --- Conclusion and Future Work --- p.106 / Bibliography --- p.109 / Chapter A --- The Rule Sets Discovered --- p.116 / Chapter A.1 --- The Best Rule Set Learned from the Iris Database --- p.116 / Chapter A.2 --- The Best Rule Set Learned from the Monk Database --- p.116 / Chapter A.2.1 --- Monkl --- p.116 / Chapter A.2.2 --- Monk2 --- p.117 / Chapter A.2.3 --- Monk3 --- p.119 / Chapter A.3 --- The Best Rule Set Learned from the Fracture Database --- p.120 / Chapter A.3.1 --- Type I Rules: About Diagnosis --- p.120 / Chapter A.3.2 --- Type II Rules : About Operation/Surgeon --- p.120 / Chapter A.3.3 --- Type III Rules : About Stay --- p.122 / Chapter A.4 --- The Best Rule Set Learned from the Scoliosis Database --- p.123 / Chapter A.4.1 --- Rules for Classification --- p.123 / Chapter A.4.2 --- Rules for Treatment --- p.126 / Chapter B --- The Grammar used for the fracture and Scoliosis databases --- p.128 / Chapter B.1 --- The grammar for the fracture database --- p.128 / Chapter B.2 --- The grammar for the Scoliosis database --- p.128

A computational study has been carried out to investigate smoldering ignition and propagation in polyurethane foam. The onedimensional, transient, governing equations for smoldering combustion in a porous fuel are solved accounting for improved solid-phase chemical kinetics. A systematic methodology for the determination of solid-phase kinetics suitable for numerical models has been developed and applied to the simulation of smoldering combustion. This methodology consists in the correlation of a mathematical representation of a reaction mechanism with data from previous thermogravimetric experiments. Geneticalgorithm and trail-and-error techniques are used as the optimization procedures. The corresponding kinetic parameters for two different mechanisms of polyurethane foam smoldering kinetics are quantified: a previously proposed 3-step mechanism and a new 5-step mechanism. These kinetic mechanisms are used to model one-dimensionalsmoldering combustion, numerically solving for the solid-phase and gasphase conservation equations in microgravity with a forced flow of oxidizer gas. The results from previously conducted microgravity experiments with flexible polyurethane foam are used for calibration and testing of the model predictive capabilities. Both forward and opposed smoldering configurations are examined. The model describes well both opposed and forward propagation. Specifically, the model predicts the reaction-front thermal and species structure, the onset of smoldering ignition, and the propagation rate. The model results reproduce the most important features of the smolder process and represent a significant step forward in smoldering combustion modeling.

The prevalence of digital manufacturing in creating increasingly complex products with small batch sizes, requires effective methods for production process planning. Toolpath generation is one of the challenges for manufacturing technologies that function based on the controlled movement of an end effector against a workpiece. The current approaches for determining suitable tool paths are highly dependent on machine structure, manufacturing technology and product geometry. This dependence can be very expensive in a volatile production environment where the products and the resources change quickly. In this research, a novel approach for the flexible generation of toolpaths using a mathematical formulation of the desired objective is proposed. The approach, based on optimisation techniques, is developed by discretising the product space into a number of grid points and determining the optimal sequence of the tool tip visiting these points. To demonstrate the effectiveness of the approach, the context of milling machining has been chosen and a genetic algorithm has been developed to solve the optimisation problem. The results show that with meta-heuristic methods, flexible tool paths can indeed be generated for industrially relevant parts using existing computational power. Future computing platforms, including quantum computers, could extend the applicability of the proposed approach to much more complex domains for instantaneous optimisation of the detailed manufacturing process plan.

Um importante problema enfrentado por engenheiros é a busca por soluções ótimas para problemas com um grande número de soluções possíveis. Neste trabalho, estudamos métodos otimização probabilísticos baseados em algoritmos genéticos, propostos inicialmente para o estudo de sistemas biológicos. Propomos algumas alterações do método de otimização por algoritmos genéticos tradicional, utilizando técnicas estatísticas de planejamento experimental, que resultaram em uma melhoria da convergência, percebida, não só na velocidade de convergência, como no número de possibilidades de soluções diferentes analisadas. Como resultado, é proposto um algoritmo que cobre o domínio de atuação dos métodos por algoritmos genéticos e do método por gradientes, permitindo uma melhor sintonização do otimizador com o problema. / A important problem faced by engineers is the search of optimal solutions for problems with a great number of possible solutions. Throughout this work, it is studied stochastic optimizers based on genetic algorithms, applied initially to the study of biological systems. Some alterations on the traditional genetic algorithms based optimizer are proposed through the use of experiment design techniques, which resulted in a improvement of the convergence that can be perceived, not only in the convergence speed, but on the number of solutions analyzed as well. As a consequence, a algorithm is proposed, covering both the traditional genetic algorithms based optimizer and the gradient method domains, allowing a better tuning of the optimizer to the problem.

Este trabalho realiza uma análise dos custos de bombeamento no Sub-sistema de abastecimento de água Jardim Cruzeiro do Sul, na cidade de São Carlos/SP. Assim, tendo em vista a atual perspectiva de racionamento de energia elétrica no país, estudos fazem-se necessários no sentido de otimizar a operação de sistemas de abastecimento de água propiciando o uso mais racional de energia elétrica. Atualmente, as companhias responsáveis pelo fornecimento de água para abastecimento comprometem grande parcela do orçamento com os custos de energia elétrica, sendo a maior parte desses custos destinados à operação de bombas. Utiliza os Algoritmos Genéticos como ferramentas na determinação da operação otimizada em termos da melhor estratégia de funcionamento das bombas, cujo problema é caracterizado pelo seu elevado grau de complexidade. / This work conducts an analysis of the pumping costs in the Sub-system of water distribution on Jardim Cruzeiro do Sul, in the city of São Carlos/SP. Thus, in view of the current perspective of rationing of electric energy in the country, studies aimed at an optimize operation of water supply systems propitiating the best rational use of electric energy. At this moment, the responsible companies of the water supply service use a great portion of the budget with the energy costs, being the largest portion of the expenses in the pumps operation. It uses the Genetic Algorithms as tools in the determination of the optimized operation in terms of the best strategy of pumps operation, whose problem is characterized for its high degree complexity.

Wang Tong. / Thesis submitted in: June 2004. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2005. / Includes bibliographical references (leaves 64-69). / Abstracts in English and Chinese. / ABSTRACT --- p.I / 摘要 --- p.IV / ACKNOWLEDGMENTS --- p.VI / TABLE OF CONTENTS --- p.VIII / LIST OF FIGURES --- p.XIIV / Chapter CHAPTER 1 --- INTRODUCTION --- p.1 / Chapter 1.1 --- Hough Transform --- p.2 / Chapter 1.2 --- Template Matching --- p.3 / Chapter 1.3 --- Genetic Algorithms --- p.4 / Chapter 1.4 --- Outline of the Thesis --- p.6 / Chapter CHAPTER 2 --- HOUGH TRANSFORM AND ITS COMMON VARIANTS --- p.7 / Chapter 2.1 --- Hough Transform --- p.7 / Chapter 2.1.1 --- What is Hough Transform --- p.7 / Chapter 2.1.2 --- Parameter Space --- p.7 / Chapter 2.1.3 --- Accumulator Array --- p.9 / Chapter 2.2 --- Gradient-based Hough Transform --- p.10 / Chapter 2.2.1 --- Direction of Gradient --- p.11 / Chapter 2.2.2 --- Accumulator Array --- p.14 / Chapter 2.2.3 --- Peaks in the accumulator array --- p.16 / Chapter 2.2.4 --- Performance of Gradient-based Hough Transform --- p.18 / Chapter 2.3 --- Generalized Hough Transform (GHT) --- p.19 / Chapter 2.3.1 --- What Is GHT --- p.19 / Chapter 2.3.2 --- R-table of GHT --- p.20 / Chapter 2.3.3 --- GHT Procedure --- p.21 / Chapter 2.3.4 --- Analysis --- p.24 / Chapter 2.4 --- Edge Detection --- p.25 / Chapter 2.4.1 --- Gradient-Based Method --- p.25 / Chapter 2.4.2 --- Laplacian of Gaussian --- p.29 / Chapter 2.4.3 --- Canny edge detection --- p.30 / Chapter CHAPTER 3 --- PROBABILISTIC MODELS --- p.33 / Chapter 3.1 --- Randomized Hough Transform (RHT) --- p.33 / Chapter 3.1.1 --- Basics of the RHT --- p.33 / Chapter 3.1.2 --- RHT algorithm --- p.34 / Chapter 3.1.3 --- Advantage of RHT --- p.37 / Chapter 3.2 --- Genetic Model --- p.37 / Chapter 3.2.1 --- Genetic algorithm mechanism --- p.38 / Chapter 3.2.2 --- A Genetic Algorithm for Primitive Extraction --- p.39 / Chapter CHAPTER 4 --- PROPOSED ARBITRARY SHAPE DETECTION --- p.42 / Chapter 4.1 --- Randomized Generalized Hough Transform --- p.42 / Chapter 4.1.1 --- R-table properties and the general notion of a shape --- p.42 / Chapter 4.1.2 --- Using pairs of edges --- p.44 / Chapter 4.1.3 --- Extend to Arbitrary shapes --- p.46 / Chapter 4.2 --- A Genetic algorithm with the Hausdorff distance --- p.47 / Chapter 4.2.1 --- Hausdorff distance --- p.47 / Chapter 4.2.2 --- Chromosome strings --- p.48 / Chapter 4.2.3 --- Discussion --- p.51 / Chapter CHAPTER 5 --- EXPERIMENTAL RESULTS AND COMPARISONS --- p.52 / Chapter 5.1 --- Primitive extraction --- p.53 / Chapter 5.2 --- Arbitrary Shape Detection --- p.54 / Chapter 5.3 --- Summary of the Experimental Results --- p.60 / Chapter CHAPTER 6 --- CONCLUSIONS --- p.62 / Chapter 6.1 --- Summary --- p.62 / Chapter 6.2 --- Future work --- p.63 / BIBLIOGRAPHY --- p.64

O objetivo do trabalho é estudar a viabilidade de tratar problemas de otimização, considerados intratáveis, através de Algoritmos Genéticos, desenvolvendo critérios para a avaliação qualitativa de um Algoritmo Genético. Dentro deste tema, abordam-se estudos sobre complexidade, classes de problemas, análise e desenvolvimento de algoritmos e Algoritmos Genéticos, este ultimo sendo objeto central do estudo. Como produto do estudo deste tema, é proposto um método de desenvolvimento de Algoritmos Genéticos, utilizando todo o estudo formal de tipos de problemas, desenvolvimento de algoritmos aproximativos e análise da complexidade. O fato de um problema ser teoricamente resolvível por um computador não é suficiente para o problema ser na prática resolvível. Um problema é denominado tratável se no pior caso possui um algoritmo razoavelmente eficiente. E um algoritmo é dito razoavelmente eficiente quando existe um polinômio p tal que para qualquer entrada de tamanho n o algoritmo termina com no máximo p(n) passos [SZW 84]. Já que um polinômio pode ser de ordem bem alta, então um algoritmo de complexidade polinomial pode ser muito ineficiente. Genéticos é que se pode encontrar soluções aproximadas de problemas de grande complexidade computacional mediante um processo de evolução simulada[LAG 96]. Como produto do estudo deste tema, é proposto um método de desenvolvimento de Algoritmos Genéticos com a consciência de qualidade, utilizando todo o estudo formal de tipos de problemas, desenvolvimento de algoritmos aproximativos e análise da complexidade. Uma axiomatização tem o propósito de dar a semântica do algoritmo, ou seja, ela define, formalmente, o funcionamento do algoritmo, mais especificamente das funções e procedimentos do algoritmo. E isto, possibilita ao projetista de algoritmos uma maior segurança no desenvolvimento, porque para provar a correção de um Algoritmo Genético que satisfaça esse modelo só é necessário provar que os procedimentos satisfazem os axiomas. Para ter-se consciência da qualidade de um algoritmo aproximativo, dois fatores são relevantes: a exatidão e a complexidade. Este trabalho levanta os pontos importantes para o estudo da complexidade de um Algoritmo Genético. Infelizmente, são fatores conflitantes, pois quanto maior a exatidão, pior ( mais alta) é a complexidade, e vice-versa. Assim, um estudo da qualidade de um Algoritmo Genético, considerado um algoritmo aproximativo, só estaria completa com a consideração destes dois fatores. Mas, este trabalho proporciona um grande passo em direção do estudo da viabilidade do tratamento de problemas de otimização via Algoritmos Genéticos. / The objective of the work is to study the viability of treating optimization problems, considered intractable, through Genetic Algorithms, developing approaches for the qualitative evaluation of a Genetic Algorithm. Inside this theme, approached areas: complexity, classes of problems, analysis and development of algorithms and Genetic Algorithms, this last one being central object of the study. As product of the study of this theme, a development method of Genetic Algorithms is proposed, using the whole formal study of types of problems, development of approximate algorithms and complexity analysis. The fact that a problem theoretically solvable isn’t enough to mean that it is solvable in pratice. A problem is denominated easy if in the worst case it possesses an algorithm reasonably efficient. And an algorithm is said reasonably efficient when a polynomial p exists such that for any entrance size n the algorithm terminates at maximum of p(n) steps [SZW 84]. Since a polynomial can be of very high order, then an algorithm of polynomial complexity can be very inefficient. The premise of the Genetic Algorithms is that one can find approximate solutions of problems of great computational complexity by means of a process of simulated evolution [LAG 96]. As product of the study of this theme, a method of development of Genetic Algorithms with the quality conscience is proposed, using the whole formal study of types of problems, development of approximate algorithms and complexity analysis. The axiom set has the purpose of giving the semantics of the algorithm, in other words, it defines formally the operation of the algorithm, more specifically of the functions and procedures of the algorithm. And this, facilitates the planner of algorithms a larger safety in the development, because in order to prove the correction of a Genetic Algorithm that satisfies that model it is only necessary to prove that the procedures satisfy the axioms. To have conscience of the quality of an approximate algorithm, two factors are important: the accuracy and the complexity. This work lifts the important points for the study of the complexity of a Genetic Algorithm. Unhappily, they are conflicting factors, because as larger the accuracy, worse (higher) it is the complexity, and vice-versa. Thus, a study of the quality of a Genetic Algorithm, considered an approximate algorithm, would be only complete with the consideration of these two factors. But, this work provides a great step in direction of the study of the viability of the treatment of optimization problems through Genetic Algorithms.

O mapeamento genético é constituído por procedimentos experimentais e estatísticos que buscam detectar genes associados à etiologia e regulação de doenças, além de estimar os efeitos genéticos e as localizações genômicas correspondentes. Considerando delineamentos experimentais que envolvem cruzamentos controlados de animais ou plantas, diferentes formulações de modelos de regressão podem ser adotados na identificação de QTLs (do inglês, quantitative trait loci), incluindo seus efeitos principais e possíveis efeitos de interação (epistasia). A dificuldade nestes casos de mapeamento é a comparação de modelos que não necessariamente são encaixados e envolvem um espaço de busca de alta dimensão. Para este trabalho, descrevemos um método geral para melhorar a eficiência computacional em mapeamento simultâneo de múltiplos QTLs e de seus efeitos de interação. A literatura tem usado métodos de busca exaustiva ou busca condicional. Propomos o uso do algoritmo genético para pesquisar o espaço multilocos, sendo este mais útil para genomas maiores e mapas densos de marcadores moleculares. Por meio de estudos de simulações mostramos que a busca baseada no algoritmo genético tem eficiência, em geral, mais alta que aquela de um método de busca condicional e que esta eficiência é comparável àquela de uma busca exaustiva. Na formalização do algoritmo genético pesquisamos o comportamento de parâmetros tais como: probabilidade de recombinação, probabilidade de mutação, tamanho amostral, quantidade de gerações, quantidade de soluções e tamanho do genoma, para diferentes funções objetivo: BIC (do inglês, Bayesian Information Criterion), AIC (do inglês, Akaike Information Criterion) e SSE, a soma de quadrados dos resíduos de um modelo ajustado. A aplicação das metodologias propostas é também considerada na análise de um conjunto de dados genotípicos e fenotípicos de ratos provenientes de um delineamento F2. / Genetic mapping is defined in terms of experimental and statistical procedures applied for detection and localization of genes associated to the etiology and regulation of diseases. Considering experimental designs in controlled crossings of animals or plants, different formulations of regression models can be adopted in the identification of QTL\'s (Quantitative Trait Loci) to the inclusion of the main and interaction effects between genes (epistasis). The difficulty in these approaches of gene mapping is the comparison of models that are not necessarily nested and involves a multiloci search space of high dimension. In this work, we describe a general method to improve the computational efficiency in simultaneous mapping of multiples QTL\'s and their interactions effects. The literature has used methods of exhausting search or conditional search. We consider the genetic algorithm to search the multiloci space, looking for epistatics loci distributed on the genome. Compared to the others procedures, the advantage to use such algorithm increases more for set of genes bigger and dense maps of molecular markers. Simulation studies have shown that the search based on the genetic algorithm has efficiency, in general, higher than the conditional search and that its efficiency is comparable to that one of an exhausting search. For formalization of the genetic algorithm we consider different values of the parameters as recombination probability, mutation probability, sample size, number of generations, number of solutions and size of the set of genes. We evaluate different objective functions under the genetic algorithm: BIC, AIC and SSE. In addition, we used the sample phenotypic and genotypic data bank. Briefly, the study examined blood pressure variation before and after a salt loading experiment in an intercross (F2) progeny.

Tong, Fung Ling. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2007. / Includes bibliographical references (leaves 94-96). / Abstracts in English and Chinese. / Abstract --- p.i / 摘要 --- p.iii / Acknowledgement --- p.iii / Table of Contents --- p.iv / List of Figures --- p.vii / List of Tables --- p.x / Chapter Chapter 1 - --- Introduction --- p.1 / Chapter Chapter 2 - --- State of the art in Vision-based Localization --- p.6 / Chapter 2.1 --- Extended Kalman Filter-based Localization --- p.6 / Chapter 2.1.1 --- Overview of the EKF algorithm --- p.6 / Chapter 2.1.2 --- Process of the EKF-based localization algorithm --- p.8 / Chapter 2.1.3 --- Recent EKF-based vision-based localization algorithms --- p.10 / Chapter 2.1.4 --- Advantages of the EKF-based localization algorithms --- p.11 / Chapter 2.1.5 --- Disadvantages of the EKF-based localization algorithm --- p.11 / Chapter 2.2 --- Monte Carlo Localization --- p.12 / Chapter 2.2.1 --- Overview of MCL --- p.12 / Chapter 2.2.2 --- Recent MCL-based localization algorithms --- p.14 / Chapter 2.2.3 --- Advantages of the MCL-based algorithm --- p.15 / Chapter 2.2.4 --- Disadvantages of the MCL-based algorithm --- p.16 / Chapter 2.3 --- Summary --- p.16 / Chapter Chapter 3 - --- Vision-based Localization as an Optimization Problem --- p.18 / Chapter 3.1 --- "Relationship between the World, Camera and Robot Body Coordinate System" --- p.18 / Chapter 3.2 --- Formulation of the Vision-based Localization as an Optimization Problem --- p.21 / Chapter 3.3 --- Summary --- p.26 / Chapter Chapter 4 - --- Existing Search Algorithms --- p.27 / Chapter 4.1 --- Overview of the Existing Search Algorithms --- p.27 / Chapter 4.2 --- Search Algorithm for the Proposed Objective Function --- p.28 / Chapter 4.3 --- Summary --- p.30 / Chapter Chapter 5 - --- Proposed Vision-based Localization using Genetic Algorithm --- p.32 / Chapter 5.1 --- Mechanism of Genetic Algorithm --- p.32 / Chapter 5.2 --- Formation of Chromosome --- p.35 / Chapter 5.3 --- Fitness Function --- p.39 / Chapter 5.4 --- Mutation and Crossover --- p.40 / Chapter 5.5 --- Selection and Stopping Criteria --- p.42 / Chapter 5.6 --- Adaptive Search Space --- p.44 / Chapter 5.7 --- Overall Flow of the Proposed Algorithm --- p.46 / Chapter 5.8 --- Summary --- p.47 / Chapter Chapter 6 - --- Experimental Results --- p.48 / Chapter 6.1 --- Test Robot --- p.48 / Chapter 6.2 --- Simulator --- p.49 / Chapter 6.2.1 --- Camera states simulation --- p.49 / Chapter 6.2.2 --- Oscillated walking motion simulation --- p.50 / Chapter 6.2.3 --- Input images simulation --- p.50 / Chapter 6.3 --- Computer for simulations --- p.51 / Chapter 6.4 --- Position and Orientation errors --- p.51 / Chapter 6.5 --- Experiment 1 一 Feature points with quantized noise --- p.53 / Chapter 6.5.1 --- Setup --- p.53 / Chapter 6.5.2 --- Results --- p.56 / Chapter 6.6 --- Experiment 2 一 Feature points added with Gaussian noise --- p.62 / Chapter 6.6.1 --- Setup --- p.62 / Chapter 6.6.2 --- Results --- p.62 / Chapter 6.7 --- Experiment 3 一 Noise reduction performance of the adaptive search space strategy --- p.77 / Chapter 6.7.1 --- Setup --- p.77 / Chapter 6.7.2 --- Results --- p.79 / Chapter 6.8 --- Experiment 4 一 Comparison with benchmark algorithms --- p.83 / Chapter 6.8.1 --- Setup --- p.83 / Chapter 6.8.2 --- Results --- p.85 / Chapter 6.9 --- Discussions --- p.88 / Chapter 6.10 --- Summary --- p.90 / Chapter Chapter 7- --- Conclusion --- p.91 / References --- p.94

Um dos problemas fundamentais em finanças é a escolha de ativos para investimento. O primeiro método para solucionar este problema foi desenvolvido por Markowitz em 1952 com a análise de como a variância dos retornos de um ativo impacta no risco do portifólio no qual o mesmo está inserido. Apesar da importância de sua contribuição, o método desenvolvido para a otimização de carteiras não leva em consideração características como a existência de lotes de compra para os ativos e a existência de custos de transação. Este trabalho apresenta uma abordagem alternativa para o problema de otimização de carteiras utilizando algoritmos genéticos. Para tanto são utilizados três algoritmos, o algoritmo genético simples, o algoritmo genético multiobjetivo (Multi Objective Genetic Algorithm - MOGA) e o algoritmo genético de ordenação não dominante (Non Dominated Sorting Genetic Algorithm - NSGA II). O desempenho apresentado pelos algoritmos genéticos neste trabalho mostram a perspectiva para a solução desse problema tão importante e complexo, obtendo-se soluções de alta qualidade e com menor esforço computacional. / One of the basic problems in finance is the choice of assets for investment. The first method to solve this problem was developed by Markowitz in 1952 with the analysis of how the variance of the returns of an asset impacts in the portfolio risk in which the same is inserted. Despite the importance of its contribution, the method developed for the portfolio optimization does not consider characteristics as the existence of round lots and transaction costs. This work presents an alternative approach for the portfolio optimization problem using genetic algorithms. For that three algorithms are used, the simple genetic algorithm, the multi objective genetic algorithm (MOGA) and the non dominated sorting genetic algorithm (NSGA II). The performance presented for the genetic algorithms in this work shows the perspective for the solution of this so important and complex problem, getting solutions of high quality and with lesser computational effort.