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Effective evaluation of losses to buildings affected by flood
Tuscher, Martin ; Schneiderová-Heralová,, Renáta (referee) ; Kocanda,, Pavel (referee) ; Zeleňáková,, Martina (referee) ; Hanák, Tomáš (advisor)
This doctoral thesis deals with the valuation of damage to buildings affected by floods. In its individual parts, it researches floods as a risk, focuses on the damage caused by this natural phenomenon and examines the methods used to assess the damages caused by floods. With the beginnings of human settlement, the vicinity of rivers has been inhabited for the many befits that watercourses bring. However, there are many dangers associated with this, especially the ones associated with the spillage of riverbeds – with floods. This phenomenon causes considerable damage to property, the environment or human health and lives. There are many measures to eliminate the risk of flooding, or at least mitigate its effects. This work further researches the mitigating of impacts – it examines the methods of determining the amount of damage to buildings caused by floods, looks for factors and parameters influencing the amount of damage and focuses on streamlining these methods. The aim of the thesis is to find a suitable methodology/model that can automate the calculation of the amount of damage, or in other words, to find a quick and at the same time sufficiently accurate solution to this problem. The main output of the thesis is the equation of the damage curve and a model for the amount of damage calculation based on the principle of damage curves using the hybrid genetic algorithm. Another output is a practical tool that works on the basis of the said algorithm and automatically calculates the amount of damage to the building when entering very basic information about the damaged object.
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The Parallel Genetic Algorithm for Multicore Systems
Vrábel, Lukáš ; Šimek, Václav (referee) ; Jaroš, Jiří (advisor)
Genetický algoritmus je optimalizačná metóda zameraná na efektívne hľadanie riešení rozličných problémov. Je založená na princípe evolúcie a prirodzeného výberu najschopnejších jedincov v prírode. Keďže je táto metóda výpočtovo náročná, bolo vymyslených veľa spôsobov na jej paralelizáciu. Avšak väčšina týchto metód je z historických dôvodov založená na superpočítačoch alebo rozsiahlych počítačových systémoch. Moderný vývoj v oblasti informačných technológií prináša na trh osobných počítačov stále lacnejšie a výkonnejšie viacjadrové systémy. Táto práca sa zaoberá návrhom nových metód paralelizácie genetického algoritmu, ktoré sa snažia naplno využiť možnosti práve týchto počítačových systémov. Tieto metódy sú následne naimplementované v programovacom jazyku C za využitia knižnice OpenMP určenej na paralelizáciu. Implementácia je následne použitá na experimentálne ohodnotenie rozličných charakteristík každej z prezentovaných metód (zrýchlenie oproti sekvenčnej verzii, závislosť konvergencie výsledných hodnôt od miery paralelizácie alebo od vyťaženia procesoru, ...). V poslednej časti práce sú prezentované porovnania nameraných hodnôt a závery vyplývajúce z týchto meraní. Následne sú prediskutované možné vylepšenia daných metód vyplývajúce z týchto záverov, ako aj možnosti spracovania väčšieho množstva charakteristík na presnejšie ohodnotenie efektivity paralelizácie genetických algoritmov.
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Structural Design Using Cellular Automata
Bezák, Jakub ; Vašíček, Zdeněk (referee) ; Bidlo, Michal (advisor)
The aim of this paper is to introduce the readers to the field of cellular automata, their design and their usage for structural design. Genetic algorithms are usually involved in designing complicated cellular automata, and because of that they are also briefly described here. For the purposes of this work sorting networks are considered as suitable structures to be designed using cellular automata, however, they are not a part of the automata but they are generated separately by modified rules of a local transition function.
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Creating Timetables Using Genetic Algorithms
Horký, Aleš ; Matoušek, Jiří (referee) ; Minařík, Miloš (advisor)
This bachelor thesis contains design and implementation of two-phase genetic algorithm intended for creating timetable schedules at primary schools. The algorithm is designed for maximum reduction of state space of solved problem without decrease of its universality. The implementated program in C++ language is applicable for creating timetable schedules at small and medium sized schools.
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Evolutionary algorithms
Bortel, Martin ; Karásek, Jan (referee) ; Burget, Radim (advisor)
Thesis describes main attributes and principles of Evolutionary and Genetic algorithms. Crossover, mutation and selection are described as well as termination options. There are examples of practical use of evolutionary and genetic algorithms. Optimization of distribution routes using PHP&MySQL and Google Maps API technologies.
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The use of genetic algorithm for edge detection in medical images
Slobodník, Michal ; Švrček, Martin (referee) ; Hrubeš, Jan (advisor)
This work deals with the possibilities of employing a genetic algorithm to edge detection. There is introduced a project which uses enhanced image divided into sub-regions, on which detection by genetic algorithm is applied. To achieving our goals are used individuals in two-dimensional bit arrays, for which are specially adjusted mutation and crossover operators. Cost minimization approach is used as fitness function. The project was created and tested in Matlab environment.
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Traveling Salesman Problem
Šůstek, Martin ; Snášelová, Petra (referee) ; Zbořil, František (advisor)
This thesis is focused on modification of known principles ACO and GA to increase their performance. Thesis includes two new principles to solve TSP. One of them can be used as an initial population generator. The appendix contains the implementation of the application in Java. The description of this application is also part of the thesis. One part is devoted to optimization in order to make methods more efficient and produce shorter paths. In the end of the thesis are described experiments and their results with different number of places from 101 up to 3891.
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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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Application for Generating of School Schedules
Fiala, Jan ; Horáček, Jan (referee) ; Rozman, Jaroslav (advisor)
This work deals with problem of generating school schedules. The solution is based on three heuristic algorithms (hill-climbing, simulated annealing, genetic algorithm) and is fully implemented in JAVA. It provides a comparison of implemented heuristic algorithms including description of their pros and cons.
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Optimal control of intersection signaling
Tinka, Petr ; Šoustek, Petr (referee) ; Kůdela, Jakub (advisor)
This master´s thesis is about the control of a traffic light. In the first, theoretical part of this work, a description of this problem introduce. The following is an overview of the optimization methods used for this problem. The practical part deals with the modeling of the network of traffic lights in program SUMO and the implementation of selected optimization methods on this. Finally, a comparison of results is made.
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