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Parallel genetic algorithm
Trupl, Jan ; Kobliha, Miloš (referee) ; Jaroš, Jiří (advisor)
The thesis describes design and implementation of various evolutionary algorithms, which were enhanced to use the advantages of parallelism on the multiprocessor systems along with ability to run the computation on different machines in a computer network. The purpose of these algorithms is to find the global extreme of function of $n$ variables. In the thesis, there are demonstrated various optimization problems, and their effective solution with the help of evolutionary algorithms. There are also described interface libraries MPI(Message Passing Interface) and OpenMP, in the extent needed to understand the problematic of parallel evolutionary algorithms.
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Rainfall runoff process in time of climate change
Benáčková, Kateřina ; Janál,, Petr (referee) ; Marton, Daniel (advisor)
The aim of The Diploma Thesis was to compile a conceptual rainfall-runoff model, that would be eligible to model discharge in conditions of climate changes. After thorough verifications of possible variants, user program Runoff Prophet that is eligible to simulate discharge in closing profile of any river basin was compiled within this paper. Runoff Prophet is deterministic lumped model with monthly computation time step and from the hydrologic phenomena it takes soil moisture, evapotranspiration, groundwater flow and the watercourse flow into account. Its calibration is based on the differential evolution principle with Nash–Sutcliffe model efficiency coefficient as the calibration criterion. Developed software was tested on Vír I. catchment basin and the results of this probe were evaluated from viewpoints of air temperature, precipitation and discharge characteristics in the Dalečín measurement river cross section in distant future according to A1B SRES climate scenario, implemented in LARS-WG weather generator.
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Optimization of PID controller using evolutionary computing techniques
Kočí, Jakub ; Matoušek, Radomil (referee) ; Lang, Stanislav (advisor)
This bachelor thesis deals with using evolutionary computation for tuning up PID controller. In research part there are summarised information about regulation and another background information about quality of regulation and ITAE criterion. Practical part consist of implementing three evolutionary computation algorithms - differential evolution, evolution strategy and genetic algorithm. These and MATLAB's function ga() are compared on two systems mutually and to Ziegler-Nichols rule. Basic comparsion is followed by statistical evaluation on second system.
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Ingelligent Import of OSM into the Traffic Simulator TRASI
Muzika, Dávid ; Klusáček, Jan (referee) ; Honzík, Petr (advisor)
The thesis deals with the design and implementation of algorithms for import maps into the simulator TRASI. These algorithms are capable of import map from map portal OpenStreetMaps to the simulation environment. The work deals with adjusting the internal structure of the imported intersections, so that their structure was correct according to the rules of traffic. The work deals with the design and implementation of differential evolution for the design of the structure of intersections.
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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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Study and comparison of main kinds of evolutionary algorithms
Štefan, Martin ; Holeňa, Martin (advisor) ; Gemrot, Jakub (referee)
Evolutionary algorithms belongs among the youngest and the most progressive methods of solving difficult optimization tasks. They received huge popularity mainly due to good experimental results in optimization, a simplicity of the implementation and a high modularity, which is an ability to be modified for different problems. Among the most frequently used Evolutionary algorithms belongs Genetic Algorithm, Differential Evolution and Evolutionary Strategy. It is able to apply these algorithms and theirs variants to both continuous, discrete and mixed optimization tasks. A subject of this theses is to compare three main types of algorithms on the catalyst optimization task with mixed variables, linear constraints and experimentally evaluated fitness function.
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Parallel genetic algorithm
Trupl, Jan ; Kobliha, Miloš (referee) ; Jaroš, Jiří (advisor)
The thesis describes design and implementation of various evolutionary algorithms, which were enhanced to use the advantages of parallelism on the multiprocessor systems along with ability to run the computation on different machines in a computer network. The purpose of these algorithms is to find the global extreme of function of $n$ variables. In the thesis, there are demonstrated various optimization problems, and their effective solution with the help of evolutionary algorithms. There are also described interface libraries MPI(Message Passing Interface) and OpenMP, in the extent needed to understand the problematic of parallel evolutionary algorithms.
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Evolutionary Algorithms for Neural Networks Learning
Vosol, David ; Rozman, Jaroslav (referee) ; Zbořil, František (advisor)
Main point of this thesis is to find and compare posibilities of cooperation between evolutionary algorithms and neural network learning and their comparison with classical learning technique called backpropagation. This comparison is demonstrated with deep feed-forward neural network which is used for classification tasks. The process of optimalization is via search of optimal values of weights and biases within neural network with fixed topology. We chose three evolutionary approaches. Genetic algorithm, differential evolution and particle swarm optimization algorithm. These three approaches are also compared between each other. The demonstrating program is implemented in Python3 programming language without usage of any third parties libraries focused on deep learning.
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Fooling of Algorithms of Computer Vision
Hrabal, Matěj ; Bartl, Vojtěch (referee) ; Herout, Adam (advisor)
The goal of this work was to research existing methods of computer vision and computer recognition fooling. My focus was on group of methods called pixel attacks. Another part of my thesis talks about methods of detecting and fighting against computer vision fooling. Implementation of various pixel attack methods and methods of defending against these kinds of attacks was done using the python programming language and python library Keras. Solution that I have created works as standalone application allowing user to perform various pixel attack methods on chosen image. This tool also allows collection of statistics from performed pixel attacks and is able to detect possible attacks in these images.
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