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Tool to Design and Optimize Schedule
Málek, Jakub ; Chlubna, Tomáš (referee) ; Beran, Vítězslav (advisor)
This thesis deals with the creation of a web application for scouting courses, which should simplify the work of generating timetables for the final competency verification. The work includes the study of modern web technologies for creating web services, genetic algorithms, the design of a graphical interface that allows users to input data for generating schedules and the final evaluation of the resulting application.
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The global optimalization methods
Dudová, Aneta ; Kozumplík, Jiří (referee) ; Mézl, Martin (advisor)
This bachelor work is dedicated to advanced methods of global optimization, and especially problem traveling salesman. It focuses on the description of the problem and its various options, including graph theory, heuristic algorithms, evolutionary algorithms, in which mainly genetic algorithms and optimization by ant colonies. In conclusion, the implementation of these methods and performed testing on different data sets of algorithms that approximately solve the traveling salesman problem.
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Neuroevolution Principles and Applications
Herec, Jan ; Strnadel, Josef (referee) ; Bidlo, Michal (advisor)
The theoretical part of this work deals with evolutionary algorithms (EA), neural networks (NN) and their synthesis in the form of neuroevolution. From a practical point of view, the aim of the work is to show the application of neuroevolution on two different tasks. The first task is the evolutionary design of the convolutional neural network (CNN) architecture that would be able to classify handwritten digits (from the MNIST dataset) with a high accurancy. The second task is the evolutionary optimization of neurocontroller for a simulated Falcon 9 rocket landing. Both tasks are computationally demanding and therefore have been solved on a supercomputer. As a part of the first task, it was possible to design such architectures which, when properly trained, achieve an accuracy of 99.49%. It turned out that it is possible to automate the design of high-quality architectures with the use of neuroevolution. Within the second task, the neuro-controller weights have been optimized so that, for defined initial conditions, the model of the Falcon booster can successfully land. Neuroevolution succeeded in both tasks.
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Optimization Methods for SIMLIB/C++ Simulation Library
Chlebík, Jakub ; Janoušek, Vladimír (referee) ; Peringer, Petr (advisor)
This thesis addresses the topic of parametric optimization of simulation models. It introduces theoretical foundation of optimization and its uses in simulation analysis. Furthermore, it suggests the extension of SIMBLI/C++ library by module for optimization methods. Some of the chosen methods are then theoretically described, implemented in C++ language, demonstrates its uses and evaluates their success.
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Stochastic management storage function of water reservoir using method of artificial intelligence
Kozel, Tomáš ; Fošumpaur, Pavel (referee) ; Zezulák,, Jiří (referee) ; Starý, Miloš (advisor)
The main advantage of stochastic forecasting is fan of possible value, which deterministic method of forecasting could not give us. Future development of random process is described better by stochastic then deterministic forecasting. We can categorize discharge in measurement profile as random process. Stochastic management is worked with dispersion of controlling discharge value. In thesis is described construction and evaluation of adaptive stochastic model base on fuzzy logic, neural networks and evolution algorithm, which are used stochastic forecast from forecasting models described in thesis. The learning fuzzy model and neural network is used as replacement of classic optimization algorithm (evolution algorithm). Model was tested and validated on made up large open water reservoir. Results were evaluated and were compared with model base on traditional algorithms, which was used for 100% forecast (forecasted values are real values). The management of the large open water reservoir with storage function, which was given by stochastic adaptive managing, was logical. The main advantage of fuzzy model and neural network model is computing speed. Classical optimization model is needed much more time for same calculation as fuzzy and neural network model, therefore classic model used clusters for stochastic calculation.
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ALPS Technique in Cartesian Genetic Programming
Stanovský, Peter ; Slaný, Karel (referee) ; Sekanina, Lukáš (advisor)
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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Tool to Design and Optimize Schedule
Málek, Jakub ; Chlubna, Tomáš (referee) ; Beran, Vítězslav (advisor)
This thesis deals with the creation of a web application for scouting courses, which should simplify the work of generating timetables for the final competency verification. The work includes the study of modern web technologies for creating web services, genetic algorithms, the design of a graphical interface that allows users to input data for generating schedules and the final evaluation of the resulting application.
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|
|
Stochastic management storage function of water reservoir using method of artificial intelligence
Kozel, Tomáš ; Fošumpaur, Pavel (referee) ; Zezulák,, Jiří (referee) ; Starý, Miloš (advisor)
The main advantage of stochastic forecasting is fan of possible value, which deterministic method of forecasting could not give us. Future development of random process is described better by stochastic then deterministic forecasting. We can categorize discharge in measurement profile as random process. Stochastic management is worked with dispersion of controlling discharge value. In thesis is described construction and evaluation of adaptive stochastic model base on fuzzy logic, neural networks and evolution algorithm, which are used stochastic forecast from forecasting models described in thesis. The learning fuzzy model and neural network is used as replacement of classic optimization algorithm (evolution algorithm). Model was tested and validated on made up large open water reservoir. Results were evaluated and were compared with model base on traditional algorithms, which was used for 100% forecast (forecasted values are real values). The management of the large open water reservoir with storage function, which was given by stochastic adaptive managing, was logical. The main advantage of fuzzy model and neural network model is computing speed. Classical optimization model is needed much more time for same calculation as fuzzy and neural network model, therefore classic model used clusters for stochastic calculation.
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Neuroevolution Principles and Applications
Herec, Jan ; Strnadel, Josef (referee) ; Bidlo, Michal (advisor)
The theoretical part of this work deals with evolutionary algorithms (EA), neural networks (NN) and their synthesis in the form of neuroevolution. From a practical point of view, the aim of the work is to show the application of neuroevolution on two different tasks. The first task is the evolutionary design of the convolutional neural network (CNN) architecture that would be able to classify handwritten digits (from the MNIST dataset) with a high accurancy. The second task is the evolutionary optimization of neurocontroller for a simulated Falcon 9 rocket landing. Both tasks are computationally demanding and therefore have been solved on a supercomputer. As a part of the first task, it was possible to design such architectures which, when properly trained, achieve an accuracy of 99.49%. It turned out that it is possible to automate the design of high-quality architectures with the use of neuroevolution. Within the second task, the neuro-controller weights have been optimized so that, for defined initial conditions, the model of the Falcon booster can successfully land. Neuroevolution succeeded in both tasks.
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Optimization Methods for SIMLIB/C++ Simulation Library
Chlebík, Jakub ; Janoušek, Vladimír (referee) ; Peringer, Petr (advisor)
This thesis addresses the topic of parametric optimization of simulation models. It introduces theoretical foundation of optimization and its uses in simulation analysis. Furthermore, it suggests the extension of SIMBLI/C++ library by module for optimization methods. Some of the chosen methods are then theoretically described, implemented in C++ language, demonstrates its uses and evaluates their success.
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