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Coevolutionary Algorithm in FPGA
Hrbáček, Radek ; Vašíček, Zdeněk (referee) ; Drahošová, Michaela (advisor)
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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Coevolutionary Algorithms and Classification
Hurta, Martin ; Sekanina, Lukáš (referee) ; Drahošová, Michaela (advisor)
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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Evolutionary Design of Quantum Operator
Kraus, Pavel ; Mrázek, Vojtěch (referee) ; Bidlo, Michal (advisor)
The goal of this thesis is to utilize various evolutionary algorithms for quantum operator design in the form of unitary matrices in direct representation. Evolution strategy, differential evolution, Particle Swarm Optimization and artificial bee colony algorithms were chosen. In this thesis, the third and fourth algorithms were used for the first time in relation to quantum operator design. The experiments have shown that the utilization of direct representation gives results of acceptable quality.
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Sports car rear wing numerical optimization
Feldová, Petra ; Rudolf, Pavel (referee) ; Štefan, David (advisor)
This master’s thesis is focused on optimization of rear wing of sport car by using open-source software. The optimization of 2D profile of the rear wing is present in this thesis. Python environment was chosen for optimization and evolutionary algorithm was is used as optimization function. This algorithm is further connected to Xfoil software, which is computing aerodynamic characteristic. The ratio of the lift and drag coefficients (C_L/C_D) is chosen as parameter which considers the aerodynamic efficiency. The CFD computation of flowing around the whole car is provided in open-source software OpenFOAM. . The profile optimization results to approximately 7.9 % raise of the parameter C_L/C_D, in the same wing stability. The main benefit of this work is to use open-source software for the optimization and CFD analysis, which in future might save company’s resources by not buying expensive commercial software licenses.
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Coevolution of Image Filters and Noise Detectors
Komjáthy, Gergely ; Zachariášová, Marcela (referee) ; Drahošová, Michaela (advisor)
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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Geometric Semantic Genetic Programming
Končal, Ondřej ; Bidlo, Michal (referee) ; Sekanina, Lukáš (advisor)
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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Aerodynamics Parametric Shape Aircraft Optimization
Dofek, Ivan ; Salga, Jaroslav (referee) ; Brož,, Václav (referee) ; Fiľakovský, Karol (advisor)
The work deals with the use of geometric parameterization for shape description of some parts of the airplane. Geometric parametrization is used for creating a parametric model airfoil. This parametric model allows local deformations pobrchu profile and can easily be applied to generate the geometry of the wing or other parts letoumu. Some properties of the parametric model were tested applications in aerodynamic optimization. Furthermore, the work deals with the parametric description of the blades, the aerodynamic optimization and noise analysis. For propeller blade were created distribution function of the control parameters that can be used in aerodynamic optimization of the blades. Geometric parameterization is used for identifying the location and other characteristics of noise sources.
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Movement Abnormalities Classification using Genetic Programming
Chudárek, Aleš ; Mrázek, Vojtěch (referee) ; Drahošová, Michaela (advisor)
When suppressing the symptoms of Parkinson's disease, the correct dosage of drugs is critical for the patient. Improper dosing can either cause insufficient suppression of symptoms or, conversely, side effects, such as dyskinesia, occur with high doses. Dyskinesia is manifested by involuntary muscle movement. This work deals with the automated classification of dyskinesia from motion data recorded using a triaxial accelerometer located on the patient's body. In this work, the classifier of dyskinesia is automatically designed using Cartesian genetic programming. The designed classifier achieves very good quality of classification of severe dyskinesia (AUC = 0,94), which is a comparable result to the techniques presented in scientific literature.
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Traffic Control System
Kačic, Matej ; Burget, Radek (referee) ; Kolář, Dušan (advisor)
Main goal of this thesis is to create an application which can do a simulation of model of road traffic system based on reality and can handle to manage signal control based on the proposed algorithm so that the road traffic should have a great performance and system should have a maximum throughput. It describes a simulation model, different approaches in design of algorithm for management of the road traffic system and decribes in detail evolutionary approach for optimalization system of control crossroads.
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Evolution Algorithm Used in Chess Game
Urminský, Andrej ; Straka, Martin (referee) ; Gajda, Zbyšek (advisor)
This thesis deals with a design of an evolutionary algorithm for an artificial intelligence in a chess game. This is accomplished by use of so called genetic algorithms. Java programming language and Eclipse, an open development platform, were used for implementation of this algorithm and the graphical user interface.
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