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Portfolio Optimization Using Genetic Algorithm
Kuruc, Igor ; Hanušová, Helena (referee) ; Chvátalová, Zuzana (advisor)
This bachelor's thesis focuses on using knowledge of portfolio theory and methods of soft computing. Theoretical backgroung is provided by postmodern portfolio theory and genetic algorithms. The purpose of aplicational section is maximizing risk-return measure. The result is optimized portfolio based on required properties. All calculation are made in Matlab software
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Implementation of Mining Modules of Data Mining System on NetBeans Platform
Stríž, Rostislav ; Bartík, Vladimír (referee) ; Šebek, Michal (advisor)
Data collecting plays an important role in many aspects of today's businesses and quality information is the key to success. Process called Knowledge Discovery in Databases makes possible to extract hidden information that can be used further in our efforts. Main goal of this thesis is to describe an addition to such Data Mining System. Main objective is to create data mining module for NetBeans application, developed for demonstrational purposes by Faculty of Information Technology. New module is going to be able to mine information from Oracle database server via unusual use of Genetic Algorithm. This thesis describes the whole process of module implementation, begining with theoretical basics through coding details to final testing and summary.
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Detection of poorly differentiated cardiac arrhythmias
Kantor, Marek ; Ronzhina, Marina (referee) ; Novotná, Petra (advisor)
This thesis focusses on the detection methods of atrial fibrilation, atrial flutter and sinus rhythm from ECG. Thesis also concentrate on the description of this arrhythmias and the learning algorithms used. In this thesis are implemented several classification approaches. For extraction of features is used convolution neural network and classification artifitial neural network. Selected 1D CNN method achived classification accuracy global F1 - score is 91 %. Moreover, the proposed CNN optimized with GA appears to be fast shallow network with better accuracy than the deep network. Created model are used for classification other type of arrhythmias too.
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The GPU Based Acceleration of Neural Networks
Šimíček, Ondřej ; Jaroš, Jiří (referee) ; Petrlík, Jiří (advisor)
The thesis deals with the acceleration of backpropagation neural networks using graphics chips. To solve this problem it was used the OpenCL technology that allows work with graphics chips from different manufacturers. The main goal was to accelerate the time-consuming learning process and classification process. The acceleration was achieved by training a large amount of neural networks simultaneously. The speed gain was used to find the best settings and topology of neural network for a given task using genetic algorithm.
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Adaptive Model for Simulation of Atmospheric Pollution
Pazúriková, Jana ; Šátek, Václav (referee) ; Dvořák, Radim (advisor)
Air pollution harms the environment and human welfare. Computer models and their simulation are useful tools for deeper understanding of processes behind as they quite accurately represent the dispersion and transformation of pollutants with advection diffusion equation or by other concepts. Current models give valid results only to constrained cases of initial conditions. The general model combining the several specific models which is able to change according to input parametres and improve with training is proposed. The adaptiveness of the system is provided by decision tree as data structure with information for selection and combination process and genetic algorithm as optimization method for adjusting the tree. The evaluation of implemented system proves that the combination of models gives better results than models themselves. Even with simple specific models, the system has achieved results comparable to state-of-art models of air pollution.
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New Cellular Automata Design Techniques
Baláž, Martin ; Drábek, Vladimír (referee) ; Bidlo, Michal (advisor)
The aim of this master thesis is to introduce a new technique for the design of cellular automata which will provide a better possibilities for the implementation and solving given problems in an environment of non-uniform automata. In this work, the theoretical foundations of cellular automata have been summarized and the possibilities of their design were examined using two evolutionary principles that have commonly been used - genetic algorithm and cellular programming. Two principally different issues were selected on which the possibilities and capabilities of these techniques were proven: the synchronization problem and the system of implementation of logic gates in an environment of cellular automata. Based on a review of the implementation properties and the initial results of usage of these methods a new design method for cellular automata was created - cellular evolution. The cellular evolution with its method of "prediction of the future state of surrounding cells" provides new possibilities in the design of cellular automata since it operates with structured genes which allow the gene to be active for a variety of cellular surroundings. In the conclusion of this work, all three methods were compared on two selected problems and their abilities were summarized in a detailed overview.
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Genetic Programming for Design of Digital Circuits
Hejtmánek, Michal ; Bidlo, Michal (referee) ; Gajda, Zbyšek (advisor)
The goal of this work was the study of evolutionary algorithms and utilization of them for digital circuit design. Especially, a genetic programming and its different manipulation with building blocks is mentioned in contrast to a genetic algorithm. On the basis of this approach, I created and tested a hybrid method of electronic circuit design. This method uses spread schemes according to the genetic algorithm for the pattern problems witch are solved by the genetic programming. The method is more successful and have faster convergence to a solution in difficult electronic circuits design than a common algorithm of the genetic programming.
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