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1
Turner, Andrew. "Evolving artificial neural networks using Cartesian genetic programming." Thesis, University of York, 2015. http://etheses.whiterose.ac.uk/12035/.
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NeuroEvolution is the application of Evolutionary Algorithms to the training of Artificial Neural Networks. NeuroEvolution is thought to possess many benefits over traditional training methods including: the ability to train recurrent network structures, the capability to adapt network topology, being able to create heterogeneous networks of arbitrary transfer functions, and allowing application to reinforcement as well as supervised learning tasks. This thesis presents a series of rigorous empirical investigations into many of these perceived advantages of NeuroEvolution. In this work it is demonstrated that the ability to simultaneously adapt network topology along with connection weights represents a significant advantage of many NeuroEvolutionary methods. It is also demonstrated that the ability to create heterogeneous networks comprising a range of transfer functions represents a further significant advantage. This thesis also investigates many potential benefits and drawbacks of NeuroEvolution which have been largely overlooked in the literature. This includes the presence and role of genetic redundancy in NeuroEvolution's search and whether program bloat is a limitation. The investigations presented focus on the use of a recently developed NeuroEvolution method based on Cartesian Genetic Programming. This thesis extends Cartesian Genetic Programming such that it can represent recurrent program structures allowing for the creation of recurrent Artificial Neural Networks. Using this newly developed extension, Recurrent Cartesian Genetic Programming, and its application to Artificial Neural Networks, are demonstrated to be extremely competitive in the domain of series forecasting.
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Walker, James Alfred. "The automatic acquisition, evolution and re-use of modules in cartesian genetic programming." Thesis, University of York, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.444766.
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Kečkéš, Miroslav. "Automatizovaný návrh obrazových filtrů na základě kartézského genetického programování." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2012. http://www.nusl.cz/ntk/nusl-219455.
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The aim of this diploma thesis is using cartesian genetic programming on design image filters and creating basic structure for implement diferent type of problems. Genetic programming is rapidly growing method, which often using for solve dificult problems. This thesis analyze basic principle, way of application and implementing this method to design filters. Result of this thesis is program realize design filters define by specific parameters, overview of implementig method and achieve summary from this sphere.
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Cattani, Philip Thomas. "Extending Cartesian genetic programming : multi-expression genomes and applications in image processing and classification." Thesis, University of Kent, 2014. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.655651.
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Genetic Programming (GP) is an Evolutionary Computation technique. Genetic Programming refers to a programming strategy where an artificial population of individuals represent solutions to a problem in the form of programs, and where an iterative process of selection and reproduction is used in order to evolve increasingly better solutions. This strategy is inspired by Charles Darwin's theory of evolution through the mechanism of natural selection. Genetic Programming makes use of computational procedures analogous to some of the same biological processes which occur in natural evolution, namely, crossover, mutation, selection, and reproduction. Cartesian Genetic Programming (CGP) is a form of Genetic Programming that uses directed graphs to represent programs. It is called 'Cartesian', because this representation uses a grid of nodes that are addressed using a Cartesian co-ordinate system. This stands in contrast to GP systems which typically use a tree-based system to represent programs. In this thesis, we will show how it is possible to enhance and extend Cartesian Genetic Programming in two ways. Firstly, we show how CGP can be made to evolve programs which make use of image manipulation functions in order to create image manipulation programs. These programs can then be applied to image classification tasks as well as other image manipulation tasks such as segmentation, the creation of image filters, and transforming an input image in to a target image. Secondly, we show how the efficiency - the time it takes to solve a problem - of a CGP program can sometimes be increased by reinterpreting the semantics of a CGP genome string. We do this by applying Multi-Expression Programming to CGP.
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Minařík, Miloš. "Sebemodifikující se programy v kartézském genetickém programování." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2010. http://www.nusl.cz/ntk/nusl-237114.
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During the last years cartesian genetic programming proved to be a very perspective area of the evolutionary computing. However it has its limitations, which make its use in area of large and generic problems impossible. These limitations can be eliminated using the recent method allowing self-modification of programs in cartesian genetic programming. The purpose of this thesis is to review the development in this area done so far. Next objective is to design own solutions for solving various problems that are hardly solvable using the ordinary cartesian genetic programming. One of the problems to be considered is generating the terms of various Taylor series. Due to the fact that the solution to this problem requires generalisation, the goal is to prove that the self-modifying cartesian genetic programming scores better than classic one for this problem. Another discussed problem is using the self-modifying genetic programming for developing arbitrarily large sorting networks. In this case, the objective is to prove that self-modification brings new features to the cartesian genetic programming allowing the development of arbitrarily sized designs.
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Oliveira, Marcelo Frasson de. "Projeto de um robô cartesiano com acionamento pneumático." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2007. http://hdl.handle.net/10183/13444.
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A grande maioria dos robôs industriais disponíveis no mercado é de alto desempenho, principalmente com relação à precisão de posicionamento. Este aspecto é um dos fatores que mais influencia no seu preço final, levando em consideração toda a complexa cadeia de elementos que fazem com que o robô opere corretamente, desde os motores e componentes mecânicos, passando pela arquitetura e sistemas de controle até o sistema de programação. Tendo isto em vista, este trabalho visa projetar um robô industrial com preço mais acessível, adequado para o uso em processos industriais que não necessitem altos níveis de precisão. Para a redução de custos de fabricação e de componentes do robô, este trabalho viabiliza o uso de atuadores pneumáticos lineares como fonte motriz, pois os mesmos são relativamente baratos, leves, não poluentes, de fácil montagem e operação, além de apresentarem uma boa relação peso/potência. Para tanto, foi implementado uma estratégia de controle por modos deslizantes com objetivo de superar as dificuldades impostas pelo comportamento não-linear dos componentes pneumáticos. Com relação à redução de custos de programação e operação do robô, desenvolveu-se um ambiente de programação off-line, através de softwares de auxilio à manufatura e de engenharia usualmente encontrados em ambientes industriais. A estratégia fundamental neste trabalho, foi o desenvolvimento de uma metodologia de projeto própria, concebida especificamente para a aplicação em projetos de robôs industriais, com os atributos de facilidade de execução e modularidade das fases envolvidas. A qual, no presente trabalho, apresenta o desenvolvimento de um robô cartesiano com três graus de liberdade acionado por atuadores pneumáticos lineares.The great majority of industrial robots available in the market have high performance, especially relative to position accuracy. This aspect is one of the factors that most influence its final price, taking into account all complicated web elements that makes the robot operates in the correct form, since the actuators and the constructive part, passing by the architecture and control systems until the system of programming. According to these, the present work aims to project an industrial robot with more accessible costs, adequate to use in industrial process that not require high level of accuracy. For the reduction of manufacture and components costs of the robot, this work make viable to use of pneumatic actuators like a motive source, because are relatively cheap, light, not pollutants, easy assembly and operation, besides presenting a good relation weight/power. For such purpose, the strategy of control was implemented by sliding mode control for the objective to surpass the difficulties imposed by the non-linear behavior of the pneumatic components. About the reduction of programming and operation costs of the robot, an off-line programming environment was developed through manufacturing aided software and a software of engineering both usually found in industrial environments. The basic strategy in this work, was the development of an own methodology of project, conceived specifically for the application in projects of industrial robots, with the attributes of easiness of execution and modularization of the wrapped phases. That methodology, in the present work, presents the development of a Cartesian robot with three degrees of freedom actuated by pneumatic servo drive.
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Vaňák, Tomáš. "Využití regresních metod pro predikci dopravy." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2014. http://www.nusl.cz/ntk/nusl-236096.
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Master thesis deals with possibilities of predicting traffic situation on the macroscopic level using data, that were recorded using traffic sensors. This sensors could be loop detectors, radar detectors or cameras. The main problem discussed in this thesis is the travel time of cars. A method for travel time prediction was designed and implemented as a part of this thesis. Data from real traffic were used to test the designed method. The first objective of this thesis is to become familiar with the prediction methods that will be used. The main objective is to use the acquired knowledge to design and to implement an aplication that will predict required traffic variables.
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Klemšová, Jarmila. "Modularita v evolučním návrhu." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2011. http://www.nusl.cz/ntk/nusl-236989.
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The diploma thesis deals with the evolutionary algorithms and their application in the area of digital circuit design. In the first part, general principles of evolutionary algorithms are introduced. This part includes also the introduction of genetic algorithms and genetic programming. The next chapter describes the cartesian genetic programming and its modifications like embedded, self-modifying or multi-chromosome cartessian genetic programming. Essential part of this work consists of the design and implementation of a modularization technique for evolution circuit design. The proposed approach is evaluated using a set of standard benchmark circuits.
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Vácha, Petr. "Křížení v kartézském genetickém programování." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2012. http://www.nusl.cz/ntk/nusl-235481.
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Optimization of digital circuits still attracts much attention not only of researchers but mainly chip producers. One of new the methods for the optimization of digital circuits is cartesian genetic programming. This Master's thesis describes a new crossover operator and its implementation for cartesian genetic programming. Experimental evaluation was performed in the task of three-bit multiplier and five-bit parity circuit design.
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Husa, Jakub. "Genetické vylepšení software pro kartézské genetické programování." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2016. http://www.nusl.cz/ntk/nusl-255458.
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Genetic programming is a nature-inspired method of programming that allows an automated creation and adaptation of programs. For nearly two decades, this method has been able to provide human-comparable results across many fields. This work gives an introduction to the problems of evolutionary algorithms, genetic programming and the way they can be used to improve already existing software. This work then proposes a program able to use these methods to improve an implementation of cartesian genetic programming (CGP). This program is then tested on a CGP implementation created specifically for this project, and its functionality is then verified on other already existing implementations of CGP.
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Manfrini, Francisco Augusto Lima. "Estratégias de busca no projeto evolucionista de circuitos combinacionais." Universidade Federal de Juiz de Fora (UFJF), 2017. https://repositorio.ufjf.br/jspui/handle/ufjf/4802.
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A computação evolucionista tem sido aplicada em diversas áreas do conhecimento para a descoberta de projetos inovadores. Quando aplicada na concepção de circuitos digitais o problema da escalabilidade tem limitado a obtenção de circuitos complexos, sendo apontado como o maior problema em hardware evolutivo. O aumento do poder dos métodos evolutivos e da eficiência da busca constitui um importante passo para melhorar as ferramentas de projeto. Este trabalho aborda a computação evolutiva aplicada ao projeto de circuito lógicos combinacionais e cria estratégias para melhorar o desempenho dos algoritmos evolutivos. As três principais contribuições resultam dessa tese são: (i) o desenvolvimento de uma nova metodologia que ajuda a compreensão das causas fundamentais do sucesso/fracasso evolutivo;(ii)a proposta de uma heurística para a semeadura da população inicial; os resultados mostram que existe uma correlação entre a topologia da população inicial e a região do espaço de busca explorada; e (iii) a proposta de um novo operador de mutação denominado Biased SAM; verificou-se que esta mutação pode guiar de maneira efetiva a busca. Nos experimentos realizados o operador proposto é melhor ou equivalente ao operador de mutação tradicional. Os experimentos computacionais que validaram as respectivas contribuições foram feitos utilizando circuitos benchmark da literatura.
Evolutionary computation has been applied in several areas of knowledge for discovering Innovative designs. When applied to a digital circuit design the scalability problem has limited the obtaining of complex circuits, being pointed as the main problem in the evolvable hardware field. Increased power of evolutionary methods and efficiency of the search constitute an important step towards improving the design tool. This work approaches the evolutionary computation applied to the design of combinational logic circuits and createsstrategiestoimprovetheperformanceofevolutionaryalgorithms. The three main contributions result from this thesis are: (i) the developement of a methodology that helps to understand the success/failure of the genetic modifications that occur along the evolution; (ii) a heuristic proposed for seeding the initial population; the results showed there is a correlation between the topology of the initial population and the region of the search space which is explored. (iii) a proposal of a new mutation operator referred to as Biased SAM; it is verified that this operator can guide the search. In the experiments performed the mutation proposed is better than or equivalent to the traditional mutation. The computational experiments that prove the efficiency of the respective contributions were made using benchmark circuits of the literature.
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Staurovská, Jana. "Nástroj pro vizuální analýzu evoluce obvodů." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2012. http://www.nusl.cz/ntk/nusl-236483.
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The main goal of the master's thesis is to compose a study on cartesian genetic programming with focus on evolution of circuits and to design a concept for visualisation of this evolution. Another goal is to create a program to visualise the circuit evolution in cartesian genetic programming, its generations and chromosomes. The program is capable of visualising the changes between generations and chromosomes and comparing more chromosomes at once. Several user cases had been prepared for the resulting program.
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Končal, Ondřej. "Geometrické sémantické genetické programování." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2018. http://www.nusl.cz/ntk/nusl-385932.
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This thesis examines a conversion of a solution produced by geometric semantic genetic programming (GSGP) to an instantion of cartesian genetic programming (CGP). GSGP has proven its quality to create complex mathematical models; however, the size of these models can get problematically large. CGP, on the other hand, is able to reduce the size of given models. This thesis combinated these methods to create a subtree CGP (SCGP). The SCGP uses an output of GSGP as an input and the evolution is performed using the CGP. Experiments performed on four pharmacokinetic tasks have shown that the SCGP is able to reduce the solution size in every case. Overfitting was detected in one out of four test problems.
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Santos, Anderson Rodrigues dos. "Síntese de árvores de padrões Fuzzy através de Programação Genética Cartesiana." Universidade do Estado do Rio de Janeiro, 2014. http://www.bdtd.uerj.br/tde_busca/arquivo.php?codArquivo=8026.
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Esta dissertação apresenta um sistema de indução de classificadores fuzzy. Ao invés
de utilizar a abordagem tradicional de sistemas fuzzy baseados em regras, foi utilizado o
modelo de Árvore de Padrões Fuzzy(APF), que é um modelo hierárquico, com uma estrutura
baseada em árvores que possuem como nós internos operadores lógicos fuzzy e as folhas são
compostas pela associação de termos fuzzy com os atributos de entrada. O classificador foi
obtido sintetizando uma árvore para cada classe, esta árvore será uma descrição lógica da
classe o que permite analisar e interpretar como é feita a classificação. O método de
aprendizado originalmente concebido para a APF foi substituído pela Programação Genética
Cartesiana com o intuito de explorar melhor o espaço de busca. O classificador APF foi
comparado com as Máquinas de Vetores de Suporte, K-Vizinhos mais próximos, florestas
aleatórias e outros métodos Fuzzy-Genéticos em diversas bases de dados do UCI Machine
Learning Repository e observou-se que o classificador APF apresenta resultados
competitivos. Ele também foi comparado com o método de aprendizado original e obteve
resultados comparáveis com árvores mais compactas e com um menor número de avaliações.This work presents a system for induction of fuzzy classifiers. Instead of the traditional fuzzy based rules, it was used a model called Fuzzy Pattern Trees (FPT), which is a hierarchical tree-based model, having as internal nodes, fuzzy logical operators and the leaves are composed of a combination of fuzzy terms with the input attributes. The classifier was obtained by creating a tree for each class, this tree will be a logic class description which allows the interpretation of the results. The learning method originally designed for FPT was replaced by Cartesian Genetic Programming in order to provide a better exploration of the search space. The FPT classifier was compared against Support Vector Machines, K Nearest Neighbour, Random Forests and others Fuzzy-Genetics methods on several datasets from the UCI Machine Learning Repository and it presented competitive results. It was also compared with Fuzzy Pattern trees generated by the former learning method and presented comparable results with smaller trees and a lower number of functions evaluations.
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Hulva, Jiří. "Koevoluční algoritmus pro úlohy založené na testu." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2014. http://www.nusl.cz/ntk/nusl-236019.
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This thesis deals with the usage of coevolution in the task of symbolic regression. Symbolic regression is used for obtaining mathematical formula which approximates the measured data. It can be executed by genetic programming - a method from the category of evolutionary algorithms that is inspired by natural evolutionary processes. Coevolution works with multiple evolutionary processes that are running simultaneously and influencing each other. This work deals with the design and implementation of the application which performs symbolic regression using coevolution on test-based problems. The test set was generated by a new method, which allows to adjust its size dynamically. Functionality of the application was verified on a set of five test tasks. The results were compared with a coevolution algorithm with a fixed-sized test set. In three cases the new method needed lesser number of generations to find a solution of a desired quality, however, in most cases more data-point evaluations were required.
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Salajka, Vojtěch. "Polymorfní obrazové filtry." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2012. http://www.nusl.cz/ntk/nusl-236449.
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This thesis deals with the polymorphic image filter design. The study includes polymorphic circuits, their theoretical base and practical applications. It further focuses on the cartesian genetic programming that can be used for an evolutionary design of some types of image filters. The thesis continues with the specification of the evolutionary algorithm to be used for the design of the polymorphic image filters. The implementation of the algorithm is described in two versions -- a standard one running only on a CPU and an accelerated one that partially uses the GPU. Several polymorphic image filters are designed by means of the algorithm.
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Veřmiřovský, Jakub. "Koevoluce v evolučním návrhu obvodů." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2016. http://www.nusl.cz/ntk/nusl-255429.
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This thesis deals with evolutionary design of the digital circuits performed by a cartesian genetic programing and optimization by a coevolution. Algorithm coevolves fitness predictors that are optimized for a population of candidate digital circuits. The thesis presents theoretical basis, especially genetic programming, coevolution in genetic programming, design of the digital circuits, and deals with possibilities of the utilization of the coevolution in the combinational circuit design. On the basis of this proposal, the application designing and optimizing logical circuits is implemented. Application functionality is verified in the five test tasks. The comparison between Cartesian genetic programming with and without coevolution is considered. Then logical circuits evolved using cartesian genetic programming with and without coevolution is compared with conventional design methods. Evolution using coevolution has reduced the number of evaluation of circuits during evolution in comparison with standard cartesian genetic programming without coevolution and in some cases is found solution with better parameters (i.e. less logical gates or less delay).
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Drahošová, Michaela. "Koevoluce prediktorů fitness v kartézském genetickém programování." Doctoral thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2017. http://www.nusl.cz/ntk/nusl-412587.
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Kartézské genetické programován (CGP) je evoluc inspirovaná metoda strojového učen, která je primárně určená pro automatizovaný návrh programů a čslicových obvodů. CGP je úspěšné v řešen mnoha úloh z reálného světa. Avšak k nalezen inovativnch řešen obvykle potřebuje značný výpočetn výkon. Každý kandidátn program navržený pomoc CGP mus být spuštěn, aby se zjistilo, do jaké mry tento program řeš zadaný problém, a mohla mu být přiřazena fitness hodnota. Právě vyhodnocen fitness bývá výpočetně nejnáročnějš část návrhu pomoc CGP. Tato práce se zabývá využitm koevoluce prediktorů fitness v CGP za účelem zrychlen procesu evolučnho návrhu prováděného pomoc CGP. Prediktor fitness je malá podmnožina trénovacch dat použvaná pro rychlý odhad fitness hodnoty namsto náročného vyhodnocen objektivn fitness hodnoty. Koevoluce prediktorů fitness je optimalizačn metoda modelován fitness, která snižuje náročnost a frekvenci výpočtu fitness. V této práci je koevolučn algoritmus přizpůsoben pro CGP a jsou představeny a zkoumány tři přstupy k zakódován prediktorů fitness. Představená metoda je experimentálně vyhodnocena v pěti úlohách symbolické regrese a v úloze návrhu obrazových filtrů. Výsledky experimentů ukazuj, že pomoc této metody lze významně snžit výpočetn čas, který CGP potřebuje pro řešen zkoumané třdy úloh.
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Komjáthy, Gergely. "Koevoluce obrazových filtrů a detektorů šumu." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2014. http://www.nusl.cz/ntk/nusl-412903.
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This thesis deals with image filter design using coevolutionary algorithms. It contains a description of evolutionary algorithms, focusing on genetic programming, cartesian genetic programming and coevolution, the reader can learn about image filters too. The next chapters contain the design of image filters and noise detectors using cooperative coevolution, and the implementation and testing of the proposed filter. In the last chapter the proposed filter is compared to other filters created using evolutionary algorithms but without coevolution.
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Piňos, Michal. "Evoluční návrh konvolučních neuronových sítí." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2020. http://www.nusl.cz/ntk/nusl-417210.
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The aim of this work is to design and implement a program for automated design of convolutional neural networks (CNN) with the use of evolutionary computing techniques. From a practical point of view, this approach reduces the requirements for the human factor in the design of CNN architectures, and thus eliminates the tedious and laborious process of manual design. This work utilizes a special form of genetic programming, called Cartesian genetic programming, which uses a graph representation for candidate solution encoding.This technique enables the user to parameterize the CNN search process and focus on architectures, that are interesting from the view of used computational units, accuracy or number of parameters. The proposed approach was tested on the standardized CIFAR-10dataset, which is often used by researchers to compare the performance of their CNNs. The performed experiments showed, that this approach has both research and practical potential and the implemented program opens up new possibilities in automated CNN design.
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Pánek, Richard. "Evoluční návrh kombinačních obvodů na počítačovém clusteru." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2015. http://www.nusl.cz/ntk/nusl-264962.
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This master's thesis deals with evolutionary algorithms and how them to use to design of combinational circuits. Genetic programming especially CGP is the most applicable to use for this type of task. Furthermore, it deals with computation on computer cluster and the use of evolutionary algorithms on them. For this computation is the most suited island models with CGP. Then a new way of recombination in CGP is designed to improve them. This design is implemented and tested on the computer cluster.
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Hytychová, Tereza. "Evoluční návrh neuronových sítí využívající generativní kódování." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2021. http://www.nusl.cz/ntk/nusl-445478.
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The aim of this work is to design and implement a method for the evolutionary design of neural networks with generative encoding. The proposed method is based on J. F. Miller's approach and uses a brain model that is gradually developed and which allows extraction of traditional neural networks. The development of the brain is controlled by programs created using cartesian genetic programming. The project was implemented in Python with the use of Numpy library. Experiments have shown that the proposed method is able to construct neural networks that achieve over 90 % accuracy on smaller datasets. The method is also able to develop neural networks capable of solving multiple problems at once while slightly reducing accuracy.
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Kapusta, Vlastimil. "Nástroj pro analýzu záznamů o průběhu evoluce číslicového obvodu." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2015. http://www.nusl.cz/ntk/nusl-235004.
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This master thesis describes stochastic optimization algorithms inspired in nature that use population of individuals - evolutionary algorithms. Genetic programming and its variant - cartesian genetic programming is described in a greater detail. This thesis is further focused on the analysis and visualization of digital circuit evolution records. Existing tools for visualization of the circuit evolution were analysed, but because no suitable tool allowing complex analysis of the circuit evolution was found, a new set of functions was proposed and the principles of a new tool were formulated. These functions were implemented in form of an interactive GUI application in Java programming language. The application was described in detail and then used for analysis of digital circuit evolution records.
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Hrbáček, Radek. "Koevoluční algoritmus v FPGA." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2013. http://www.nusl.cz/ntk/nusl-236407.
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This thesis deals with the design of a hardware acceleration unit for digital image filter design using coevolutionary algorithms. The first part introduces reconfigurable logic device technology that the acceleration unit is based on. The theoretical part also briefly characterizes evolutionary and coevolutionary algorithms, their principles and applications. Traditional image filter designs are compared with the biologically inspired design methods. The hardware unit presented in this thesis exploits dual MicroBlaze system extended by custom peripherals to accelerate cartesian genetic programming. The coevolutionary image filter design is accelerated up to 58 times. The hardware platform functionality in the task of impulse noise filter design and edge detector design has been empirically analyzed.
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Drahošová, Michaela. "Symbolická regrese a koevoluce." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2011. http://www.nusl.cz/ntk/nusl-236994.
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Symbolic regression is the problem of identifying the mathematic description of a hidden system from experimental data. Symbolic regression is closely related to general machine learning. This work deals with symbolic regression and its solution based on the principle of genetic programming and coevolution. Genetic programming is the evolution based machine learning method, which automaticaly generates whole programs in the given programming language. Coevolution of fitness predictors is the optimalization method of the fitness modelling that reduces the fitness evaluation cost and frequency, while maintainig evolutionary progress. This work deals with concept and implementation of the solution of symbolic regression using coevolution of fitness predictors, and its comparison to a solution without coevolution. Experiments were performed using cartesian genetic programming.
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Jašíčková, Karolína. "Klasifikace obrazů pomocí genetického programování." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2018. http://www.nusl.cz/ntk/nusl-385980.
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This thesis deals with image classification based on genetic programming and coevolution. Genetic programming algorithms make generating executable structures possible, which allows us to design solutions in form of programs. Using coevolution with the fitness prediction lowers the amount of time consumed by fitness evaluation and, therefore, also the execution time. The thesis describes a theoretical background of evolutionary algorithms and, in particular, cartesian genetic programming. We also describe coevolutionary algorithms properties and especially the proposed method for the image classifier evolution using coevolution of fitness predictors, where the objective is to find a good compromise between the classification accuracy, design time and classifier complexity. A part of the thesis is implementation of the proposed method, conducting the experiments and comparison of obtained results with other methods.
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Paris, Paulo Cesar Donizeti. "Simulador de arquitetura para processamento de imagens usando programação genética cartesiana." Universidade Federal de São Carlos, 2013. https://repositorio.ufscar.br/handle/ufscar/560.
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Previous issue date: 2013-12-20The tools offered by the area of Mathematical Morphology are very effective when applied to the analysis of binary images, which it is of great importance in areas such as: robotic vision, visual inspection, among others. Such tools, beside to Evolutionary Computation and based on genotype-phenotypes mappings allow computational tasks be performed automatically without explicit programming, which leads to the motivation, in the search of a way of reducing the degree of difficulty often found by human experts in performing tasks of selecting linear operators to be used in morphological filters. Moreover, if such tasks require fast processing on the images, it is necessary the use of architectures implemented in hardware, which it is not too trivial to be done. In this work, a hardware architecture simulator has been implemented for image processing, based on Cartesian Genetic Programming, which automatically builds filters for processing binary images, i.e., automatically build a sequence of logical and morphological operators that produces filters to obtain an approximate of the desired images. The results obtained from several experiments of transformation of these images are presented and comparatively analyzed in relation to previous results available in the literature. Based on these results, it will be possible to study the behavior of such architecture, through the variation of the parameters of the genetic procedure in the simulator environment. Thus, it will be possible to infer if the architecture is suitable or not for a desired application, so facilitating the process of design and implementation of it in hardware.
As ferramentas oferecidas pela área de Morfologia Matemática são muito eficientes quando aplicadas na análise de imagens binárias, o que é de grande importância em áreas como: visão robótica, inspeção visual, entre outras. Tais ferramentas aliadas à Computação Evolucionária e baseadas em mapeamentos genótipo-fenótipo permite que as tarefas computacionais possam ser executadas de forma automática, sem programações explicitas, o que leva a uma motivação na busca de uma forma de redução do grau de dificuldade, muitas vezes encontrado pelos especialistas na realização de tarefas de seleção de operadores de imagem para serem utilizados em tarefas de análise. Além disso, se tais tarefas necessitarem de processamentos rápidos sobre as imagens, faz-se necessário o uso de arquiteturas implementadas em hardware, o que também não é muito trivial de serem projetadas. Assim, neste trabalho, implementa-se um simulador de arquiteturas de hardware para processamento de imagens, com base na metodologia de Programação Genética Cartesiana, que gera automaticamente filtros para o processamento de imagens binárias, ou seja, constrói-se automaticamente uma sequência de operadores lógicos e morfológicos que produzem os filtros para as imagens desejadas. Os resultados obtidos a partir de diversos estudos de casos de transformação dessas imagens são apresentados e analisadoscomparativamente em relação aos resultados anteriores disponíveis na literatura. Com base nestes resultados, é possível estudar o comportamento de tal arquitetura, através da variação dos parâmetros do procedimento genético no ambiente do simulador. Assim, é possível inferir se a arquitetura modelada será ou não adequada à aplicação desejada, logo, facilitando-se o processo de projeto e implementação em hardware.
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Matyáš, Jiří. "Využití přibližné ekvivalence při návrhu přibližných obvodů." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2017. http://www.nusl.cz/ntk/nusl-363841.
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This thesis is concerned with the utilization of formal verification techniques in the design of the functional approximations of combinational circuits. We thoroughly study the existing formal approaches for the approximate equivalence checking and their utilization in the approximate circuit development. We present a new method that integrates the formal techniques into the Cartesian Genetic Programming. The key idea of our approach is to employ a new search strategy that drives the evolution towards promptly verifiable candidate solutions. The proposed method was implemented within ABC synthesis tool. Various parameters of the search strategy were examined and the algorithm's performance was evaluated on the functional approximations of multipliers and adders with operand widths up to 32 and 128 bits respectively. Achieved results show an unprecedented scalability of our approach.
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Trefilík, Jakub. "Koevoluce obrazových filtrů a prediktorů fitness." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2015. http://www.nusl.cz/ntk/nusl-234952.
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This thesis deals with employing coevolutionary principles to the image filter design. Evolutionary algorithms are very advisable method for image filter design. Using coevolution, we can add the processes, which can accelerate the convergence by interactions of candidate filters population with population of fitness predictors. Fitness predictor is a small subset of the training set and it is used to approximate the fitness of the candidate solutions. In this thesis, indirect encoding is used for predictors evolution. This encoding represents a mathematical expression, which selects training vectors for candidate filters fitness prediction. This approach was experimentally evaluated in the task of image filters for various intensity of random impulse and salt and pepper noise design and the design of the edge detectors. It was shown, that this approach leads to adapting the number of target objective vectors for a particular task, which leads to computational complexity reduction.
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Mrázek, Vojtěch. "Akcelerace evolučního návrhu obvodů na úrovni tranzistorů na platformě Zynq." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2014. http://www.nusl.cz/ntk/nusl-236023.
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The goal of this project is to design a hardware unit that is designed to accelerate evolutionary design of digital circuits on transistor level. The project is divided to two parts. The first one describes design methods of the MOSFET circuits and issues of evolutionary algorithms. It also analyses current results in this domain and provides a new method for the design and optimization. The second part describes proposed unit that accelerates the new method on the circuit Zynq which integrates ARM processor and programmable logic. The new method functionality has been empirically analysed in the task of optimization of few circuits with more inputs. The hardware unit has been tested for designing of gates on transistor level.
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Pták, Ondřej. "Evoluční resyntéza kombinačních obvodů." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2013. http://www.nusl.cz/ntk/nusl-236208.
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This project deals with combinational digital circuits and their optimization. First there are presented main levels of abstraction utilized in the design of combinational digital circuits. Afterwards different methods are surveyed for optimization of combinational digital circuits. The next part of this project is mainly devoted to evolutionary algorithms, their common characteristics and branches: genetic algorithms, evolutionary strategies, evolutionary programming and genetic programming. The variant of genetic programming called Cartesian Genetic Programming (CGP) and the use of CGP in various areas, particularly in the synthesis and optimization of combinational logic circuits are described in detail. The project also discusses some modifications of CGP and the scalability problem of evolutionary circuit design. Consequential part of this thesis describes the method for evolution resynthesis of combinational digital circuits. There is description of design, especially the method of splitting circuits into subcircuits, and implementation details. Finally experiments with these method and their results are described.
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Kovács, Martin. "Techniky reprezentace pro evoluční návrh celulárních automatů." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2016. http://www.nusl.cz/ntk/nusl-255340.
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The aim of this thesis is to experimentally evaluate the performance of several distinct representations of transition functions for cellular automata. Cellular automata have many potential applications for simulating various phenomena (e.g. natural processes, physical systems, etc.). Parallel computation of cellular automata is based on local cell interactions. Such computation, however, may prove difficult to program the CA, which is the reason for applying evolutionary techniques for the design of cellular automata in many cases. Evolutionary algorithms, based on Darwin's theory of evolution, have been used to find human-competitive solutions to many problems. In order to perform the evolutionary design of cellular automata, special encodings of the candidate solutions are often necessary. For this purpose the performance testing of various representations of the transition functions will be investigated. In particular, table representation, conditionally matching rules, and genetic programming will be treated. The problem of square calculations in cellular automata will be considered as a case study.
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Hodaň, David. "Možnosti akcelerace symbolické regrese pomocí kartézského genetického programování." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2019. http://www.nusl.cz/ntk/nusl-403198.
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This thesis is focused on finding procedures that would accelerate symbolic regressions in Cartesian Genetic Programming. It describes Cartesian Genetic Programming and its use in the task of symbolic regression. It deals with the SIMD architecture and the SSE and AVX instruction set. Several optimizations that lead to a significant acceleration of evolution in Cartesian Genetic Programming are presented. A method of a bit-level parallel simulation that uses AVX2 vectors allows to process 256 input combinations of a logic circuit in paralell. Similarly it is possible to use a byte-level parallel simulation and work with 32 bytes when evolving an image filter. A new method of batch mutation can accelerate the evolution of combinational logic circuits thousand times depending on the problem size. For example, using a combination of these and other methods the evolution of 5 x 5b multipliers took 5.8 seconds on average on an Intel Core i5-4590 processor.
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Hojný, Ondřej. "Evoluční návrh kombinačních obvodů." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2021. http://www.nusl.cz/ntk/nusl-442801.
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This diploma thesis deals with the use of Cartesian Genetic Programming (CGP) for combinational circuits design. The work addresses the issue of optimizaion of selected logic circuts, arithmetic adders and multipliers, using Cartesian Genetic Programming. The implementation of the CPG is performed in the Python programming language with the aid of NumPy, Numba and Pandas libraries. The method was tested on selected examples and the results were discussed.
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Almeida, Manoel Aranda de. "Sistema embarcado reconfigurável de forma estática por programação genética utilizando hardware evolucionário híbrido." Universidade Federal de São Carlos, 2016. https://repositorio.ufscar.br/handle/ufscar/8000.
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The use of technology based on Field Programmable Gate Arrays (FPGAs), a reconfigurable technology, has become a frequent object of study. This technique is feasible and a promising application in the development of embedded systems, however, the difficulty in finding a flexible and efficient way to perform such an application is their bigger problem. In this work, a virtual and reconfigurable architecture (AVR) in FPGA for hardware applications is presented using a Genetic Programming Software on the development of an optimal reconfiguration for this AVR, in order to build a hardware capable of performing a given task in an embedded system. This proposal is a simple, flexible and efficient way to achieve appropriate applications in embedded systems, when compared to other reconfigurable hardware techniques. The representation of phenotype of the proposed evolutionary system is based on a bi-dimensional network function elements (EF). The GPLAB tool for MATLAB is used in Genetic Programming, and the solution found by this procedure is converted into a memory mapping to represent the best solution, where it is used to reconfigure the hardware. In the tests, GPLAB found results for logic circuits in a few generations, and for image filters containing efficient solutions, where there was little hardware occupation, especially memory, in the cases this has been presented, with a reduced chromosome size, shows a proposal efficiency.
O uso da tecnologia baseada em Field Programmable Gate Arrays (FPGAs), de forma reconfigurável, para a solução de diversos problemas atuais, tem se tornado um frequente objeto de estudo. Essa técnica é de aplicação viável e promissora na elaboração de sistemas embarcados, porém, a dificuldade em encontrar uma forma flexível e eficiente de realizar tal aplicação é o seu maior problema. Neste trabalho, é apresentada uma arquitetura virtual e reconfigurável (AVR) em FPGA para aplicações em hardware, utilizando um software de Programação Genética na elaboração de uma reconfiguração ótima para esta AVR, de forma a construir um hardware capaz de efetuar uma determinada tarefa em um sistema embarcado. Esta proposta é uma forma simples, flexível e eficiente de realizar aplicações adequadas em sistemas embarcados, quando comparada a outras técnicas de hardware reconfigurável. A representação do fenótipo no sistema evolutivo proposto se baseia em uma rede de elementos de função (EF) bidimensional. A ferramenta GPLAB, para MATLAB, é usada na Programação Genética, e a solução encontrada por esta é convertida em um mapeamento de memória com o cromossomo da melhor solução, onde este é usado para reconfigurar o hardware. Nos testes realizados, a GPLAB encontrou resultados para circuitos lógicos em poucas gerações, e para filtros de imagem encontrou soluções eficientes, onde ocorreu pouca ocupação de hardware, principalmente da memória nos casos apresentados, apresentando um cromossomo de tamanho reduzido, o que demonstra uma boa eficiência da proposta.
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Stanovský, Peter. "Technika ALPS v kartézském genetickém programování." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2009. http://www.nusl.cz/ntk/nusl-236776.
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This work introduces a brief summary of softcomputing and the solutions to NP-hard problems. It especially deals with evolution algorithms and their basic types. The next part involves the study of cartesian genetic programming, which belongs to the field of evolution algorithms, used mainly in the evolution of digital circuits, symbolic regression, etc. A special chapter is devoted to the studies of new technique Age layered population structure, which deals with the problems of premature convergence, which suggests the way of how the population could be divided into subpopulations split up according to the age criteria. Thanks to the maintaining of sufficient diversity, it achieves substantially better solutions in comparison to the classical evolution algorithms. This papier includes the suggestion of two ways of incorporation of the ALPS technique into CGP. In the next part of work there were carried out tests on the classic problems, that would be solved with evolution algorithms. These tests were made with and without using ALPS technique. In the part of work "Experimental results" there was discussed a contribution of using ALPS technique in CGP against the classic CGP.
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Hejtmánek, Michal. "Užití genetického programování v návrhu digitálních obvodů." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2008. http://www.nusl.cz/ntk/nusl-235950.
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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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Dvořáček, Petr. "Evoluční návrh pro aproximaci obvodů." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2015. http://www.nusl.cz/ntk/nusl-234958.
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In recent years, there has been a strong need for the design of integrated circuits showing low power consumption. It is possible to create intentionally approximate circuits which don't fully implement the specified logic behaviour, but exhibit improvements in term of area, delay and power consumption. These circuits can be used in many error resilient applications, especially in signal and image processing, computer graphics, computer vision and machine learning. This work describes an evolutionary approach to approximate design of arithmetic circuits and other more complex systems. This text presents a parallel calculation of a fitness function. The proposed method accelerated evaluation of 8-bit approximate multiplier 170 times in comparison with the common version. Evolved approximate circuits were used in different types of edge detectors.
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Roreček, Pavel. "Evoluční optimalizace konvolučních neuronových sítí." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2018. http://www.nusl.cz/ntk/nusl-385906.
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This Master's Thesis is focused on the principles of neural networks, primarily convolutional neural networks (CNN). It introduces the evolutionary optimization in the context of neural networks. One of existing libraries devoted to the CNN design was chosen (Keras), analysed and used in image classification tasks. An optimization technique based on cartesian genetic programming that should reduce the complexity of CNN's computation was proposed and implemented. The impact of the proposed technique on CNN behaviour was evaluated in a case study.
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Hurta, Martin. "Koevoluční algoritmy a klasifikace." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2021. http://www.nusl.cz/ntk/nusl-445529.
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The aim of this work is to automatically design a program that is able to detect dyskinetic movement features in the measured patient's movement data. The program will be developed using Cartesian genetic programming equipped with coevolution of fitness predictors. This type of coevolution allows to speed up a design performed by Cartesian genetic programming by evaluating a quality of candidate solutions using only a part of training data. Evolved classifier achieves a performance (in terms of AUC) that is comparable with the existing solution while achieving threefold acceleration of the learning process compared to the variant without the fitness predictors, in average. Experiments with crossover methods for fitness predictors haven't shown a significant difference between investigated methods. However, interesting results were obtained while investigating integer data types that are more suitable for implementation in hardware. Using an unsigned eight-bit data type (uint8_t) we've achieved not only comparable classification performance (for significant dyskinesia AUC = 0.93 the same as for the existing solutions), with improved AUC for walking patient's data (AUC = 0.80, while existing solutions AUC = 0.73), but also nine times speedup of the design process compared to the approach without fitness predictors employing the float data type, in average.
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Gajda, Zbyšek. "Evoluční přístup k syntéze a optimalizaci běžných a polymorfních obvodů." Doctoral thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2011. http://www.nusl.cz/ntk/nusl-261254.
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Tato disertační práce se zabývá evolučním návrhem a optimalizací jak běžných, tak polymorfních digitálních obvodů. V práci jsou uvedena a vyhodnocena nová rozšíření kartézského genetického programování (Cartesian Genetic Programming, CGP), která umožňují zkrácení výpočetního času a získávání kompaktnějších obvodů. Další část práce se zaměřuje na nové metody syntézy polymorfních obvodů. Uvedené metody založené na polymorfních binárních rozhodovacích diagramech a polymorfním multiplexovaní rozšiřují běžné reprezentace digitálních obvodů, a to s ohledem na začlenění polymorfních hradel. Z důvodu snížení počtu hradel v obvodech syntetizovaných uvedenými metodami je provedena evoluční optimalizace založená na CGP. Implementované polymorfní obvody, které jsou optimalizovány s využitím CGP, reprezentují nejlepší známá řešení, jestliže je jako cílové kritérium brán počet hradel obvodu.
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Kešner, Filip. "Design of Digital Circuits at Transistor Level." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2014. http://www.nusl.cz/ntk/nusl-236048.
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This work aims to design process of integrated circuits on the transistor level, specially using evolutionary algorithm. For this purpose it is necessary to choose reasonable level of abstraction during simulation, which is used for evaluation candidate solutions by fitness function. This simulation has to be fast enough to evaluate thousands of candidate solutions within seconds. This work discusses already used techniques for transistor level circuit design and it chooses useful parts for new design of faster and more reliable automated design process, which would be able to design complex logic circuits. The thesis also discusses implementation of this system and used approach with regard to encountered problems in transistor-level circuit design and optimization by evolution.
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Wiglasz, Michal. "Souběžné učení v koevolučních algoritmech." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2015. http://www.nusl.cz/ntk/nusl-234912.
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Cartesian genetic programming (CGP) is a form of genetic programming where candidate programs are represented in the form of directed acyclic graphs. It was shown that CGP can be accelerated using coevolution with a population of fitness predictors which are used to estimate the quality of candidate solutions. The major disadvantage of the coevolutionary approach is the necessity of performing many time-consuming experiments to determine the best size of the fitness predictor for the particular task. This project introduces a new fitness predictor representation with phenotype plasticity, based on the principles of colearning in evolutionary algorithms. Phenotype plasticity allows to derive various phenotypes from the same genotype. This allows to adapt the size of the predictors to the current state of the evolution and difficulty of the solved problem. The proposed algorithm was implemented in the C language and optimized using SSE2 and AVX2 vector instructions. The experimental results show that the resulting image filters are comparable with standard CGP in terms of filtering quality. The average speedup is 8.6 compared to standard CGP. The speed is comparable to standard coevolutionary CGP but it is not necessary to experimentally determine the best size of the fitness predictor while applying coevolution to a new, unknown task.
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Hovorka, Bedřich. "Hledání S-boxů pomocí evolučních algoritmů." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2010. http://www.nusl.cz/ntk/nusl-237187.
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This work deals with part of the encryption algorithm, called S-box and its development. For its development is used evolutionary computing, such as classical genetic algorithm, Estimation of Distribution Algorithm, Cartesian genetic programming and multi-criteria VEGA and SPEA algorithms. This thesis aims to test the properties of substitution boxes to its evolutionary development. Firstly, the work deals with cryptography and issues of s-boxes. There are explained basic concepts and describes the selected criteria of safety. Next chapter explains evolutionary algorithms and multi-criteria optimization. This knowledge is used to design and program implementation, which are described below. Finally discusses the application of the criteria studied. Discussed here is searching S-boxes in both single-criteria, and especially in multi-criteria genetic search.
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Hrbáček, Radek. "Automatický multikriteriální paralelní evoluční návrh a aproximace obvodů." Doctoral thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2017. http://www.nusl.cz/ntk/nusl-412591.
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Spotřeba a energetická efektivita se stává jedním z nejdůležitějších parametrů při návrhu počítačových systémů, zejména kvůli omezené kapacitě napájení u zařízení napájených bateriemi a velmi vysoké spotřebě energie rostoucích datacenter a cloudové infrastruktury. Současně jsou uživatelé ochotni do určité míry tolerovat nepřesné nebo chybné výpočty v roustoucím počtu aplikací díky nedokonalostem lidských smyslů, statistické povaze výpočtů, šumu ve vstupních datech apod. Přibližné počítání, nová oblast výzkumu v počítačovém inženýrství, využívá rozvolnění požadavků na funkčnost za účelem zvýšení efektivity počítačových systémů, pokud jde o spotřebu energie, výpočetní výkon či složitost. Aplikace tolerující chyby mohou být implementovány efektivněji a stále sloužit svému účelu se stejnou nebo mírně sníženou kvalitou. Ačkoli se objevují nové metody pro návrh přibližně počítajících výpočetních systémů, je stále nedostatek automatických návrhových metod, které by nabízely velké množství kompromisních řešení dané úlohy. Konvenční metody navíc často produkují řešení, která jsou daleko od optima. Evoluční algoritmy sice přinášejí inovativní řešení složitých optimalizačních a návrhových problémů, nicméně trpí několika nedostatky, např. nízkou škálovatelností či vysokým počtem generací nutných k dosažení konkurenceschopných výsledků. Pro přibližné počítání je vhodný zejména multikriteriální návrh, což existující metody většinou nepodporují. V této práci je představen nový automatický multikriteriální paralelní evoluční algoritmus pro návrh a aproximaci digitálních obvodů. Metoda je založena na kartézském genetickém programování, pro zvýšení škálovatelnosti byla navržena nová vysoce paralelizovaná implementace. Multikriteriální návrh byl založen na principech algoritmu NSGA-II. Výkonnost implementace byla vyhodnocena na několika různých úlohách, konkrétně při návrhu (přibližně počítajících) aritmetických obvodů, Booleovských funkcích s vysokou nelinearitou či přibližných logických obvodů pro tří-modulovou redundanci. V těchto úlohách bylo dosaženo význammých zlepšení ve srovnání se současnými metodami.
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Válek, Matěj. "Aproximativní implementace aritmetických operací v obrazových filtrech." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2021. http://www.nusl.cz/ntk/nusl-445540.
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Tato diplomová práce se zabývá aproximativní implementace aritmetických operací v obrazových filtrech. Zejména tedy využitím aproximativních technik pro úpravu způsobu násobení v netriviálním obrazovém filtru. K tomu je využito několik technik, jako použití převodu násobení s pohyblivou řadovou čárkou na násobení s pevnou řadovou čárkou, či využití evolučních algoritmů zejména kartézkého genetického programování pro vytvoření nových aproximovaných násobiček, které vykazují přijatelnou chybu, ale současně redukují výpočetní náročnost filtrace. Výsledkem jsou evolučně navržené aproximativní násobičky zohledňující distribuci dat v obrazovém filtru a jejich nasazení v obrazovém filtru a porovnání původního filtru s aproximovaným fitrem na sadě barevných obrázků.
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Jain, Sumit. "Exploiting contacts for interactive control of animated human characters." Diss., Georgia Institute of Technology, 2011. http://hdl.handle.net/1853/44817.
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One of the common research goals in disciplines such as computer graphics and robotics is to understand the subtleties of human motion and develop tools for recreating natural and meaningful motion. Physical simulation of virtual human characters is a promising approach since it provides a testbed for developing and testing control strategies required to execute various human behaviors. Designing generic control algorithms for simulating a wide range of human activities, which can robustly adapt to varying physical environments, has remained a primary challenge.
This dissertation introduces methods for generic and robust control of virtual characters in an interactive physical environment. Our approach is to use the information of the physical contacts between the character and her environment in the control design. We leverage high-level knowledge of the kinematics goals and the interaction with the surroundings to develop active control strategies that robustly adapt to variations in the physical scene. For synthesizing intentional motion requiring long-term planning, we exploit properties of the physical model for creating efficient and robust controllers in an interactive framework. The control design leverages the reference motion capture data and the contact information with the environment for interactive long-term planning. Finally, we propose a compact soft contact model for handling contacts for rigid body virtual characters. This model aims at improving the robustness of existing control methods without adding any complexity to the control design and opens up possibilities for new control algorithms to synthesize agile human motion.
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Mazuch, Martin. "Polymorfní samočinně testovatelné obvody." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2008. http://www.nusl.cz/ntk/nusl-236018.
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This Master's thesis deals with question of the development of self-checking polymorphic circuits. It deals with a traditional way of creating reliable and self-checking circuits, presenting basic principles and methods. Also a method of Cartesian Genetic Programming for development of combinational circuits is explained. This thesis describes concepts of polymorphic gates and circuits and their benefits in practical use. Some existing self-checking polymorphic circuits are presented and their self-checking capabilities are analyzed. A proposal of realization of a design system for self-checking polymorphic circuits is given. A design system has been built based on presented specification and an application allowing simulations and analysis of system-proposed solutions has been created. Variety of experiments have been performed at created system and several interesting solutions have been acquired. At the end, conclusion is given and benefits of MSc. project are discussed.
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Mrázek, Vojtěch. "Metodologie pro automatický návrh nízkopříkonových aproximativních obvodů." Doctoral thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2018. http://www.nusl.cz/ntk/nusl-412599.
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Rozšiřování moderních vestavěných a mobilních systémů napájených bateriemi zvyšuje požadavky na návrh těchto systémů s ohledem na příkon. Přestože moderní návrhové techniky optimalizují příkon, elektrická spotřeba těchto obvodů stále roste díky jejich složitosti. Nicméně existuje celá řada aplikací, kde nepotřebujeme získat úplně přesný výstup. Díky tomu se objevuje technika zvaná aproximativní (přibližné) počítání, která umožňuje za cenu zanesení malé chyby do výpočtu významně redukovat příkon obvodů. V práci se zaměřujeme na použití evolučních algoritmů v této oblasti. Ačkoliv již tyto algoritmy byly úspěšně použity v syntéze přesných i aproximativních obvodů, objevují se problémy škálovatelnosti - schopnosti aproximovat složité obvody. Cílem této disertační práce je ukázat, že aproximační logická syntéza založená na genetickém programování umožňuje dosáhnout vynikajícího kompromisu mezi spotřebou a chybou. Byla provedena analýza čtyř různých aplikacích na třech úrovních popisu. Pomocí kartézského genetického programování s modifikovanou reprezentací jsme snížili spotřebu malých obvodů popsaných na úrovni tranzistorů použitelných například v technologické knihovně. Dále jsme zavedli novou metodu pro aproximaci aritmetických obvodů, jako jsou sčítačky a násobičky, popsaných na úrovni hradel. S využitím metod formální verifikace navíc celý návrhový proces umožňuje garantovat stanovenou chybu aproximace. Tyto obvody byly využity pro významné snížení příkonu v neuronových sítích pro rozpoznávání obrázků a v diskrétní kosinově transformaci v HEVC kodéru. Pomocí nové chybové metriky nezávislé na rozložení vstupních dat jsme navrhli komplexní aproximativní mediánové filtry vhodné pro zpracování signálů. Disertační práce reprezentuje ucelenou metodiku pro návrh aproximativních obvodů na různých úrovních popisu, která navíc garantuje nepřekročení zadané chyby aproximace.
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Thomaz, Adriano. "Metodo de pontos interiores aplicado ao fluxo de potencia otimo utilizando coordenadas cartesianas." [s.n.], 2007. http://repositorio.unicamp.br/jspui/handle/REPOSIP/261144.
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Orientadores: Secundino Soares Filho, Aurelio Ribeiro Leite de OliveiraTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de Computação
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Resumo: O método de pontos interiores primal-dual é desenvolvido para o problema de fluxo de potência ótimo corrente alternada ativo e reativo. Adotou-se a representação das tensões através de coordenadas cartesianas uma vez que neste modelo a hessiana do problema é constante e a expansão em Taylor é exata para o termo de ordem dois. Antes da aplicação do método, o número de variáveis do problema é reduzido, não alterando a estrutura esparsa do problema. A matriz resultante é simétrica em estrutura e essa característica é explorada de forma eficiente reduzindo o esforço computacional por iteração. A implementação fornece um ponto de partida, uma solução inicial para ser utilizada como base e referência para futuros aprimoramentos e estudos. Permite inclusão de novos estudos de limites operacionais e físicos, particulares de cada sistema, sem a necessidade de mudanças estruturais. O desenvolvimento propõe novas idéias com técnicas de resolução já conhecidas. Os resultados dos experimentos computacionais, utilizando sistemas de teste IEEE e um sistema real brasileiro, são apresentados
Abstract: The primal dual interior point methods are developed to the AC active and reactive optimal power flow problem. The representation of the complex bus-voltages through cartesian coordinates is adopted, once the Hessian is constant and the Taylor expansion is accurate for the second order term. Before the application of the method, the number of variables of the problem is reduced. This reduction does not modify the sparse pattern of the problem. The final matrix is symmetric in structure and this feature can be exploited reducing the computational effort per iteration. The implementation gives a start point, an initial solution that can be used as a base and reference for future improvements and studies. It also allows including new studies of physical and operational limits, for each system, without the necessity of structural changes. This development proposes new ideas using solution technics already known. The computacional experiments results presented are performed for IEEE test systems and a real Brazilian system
Doutorado
Energia Eletrica
Doutor em Engenharia Elétrica