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Intelligent Energy Measurement Device
Mrázek, Vojtěch ; Sekanina, Lukáš (referee) ; Vašíček, Zdeněk (advisor)
The goal of this project is to design an energy measurement device that supports logging of historic values and offers simple analysis of the values. The proposed device enables to display actual quantities such as active and reactive power, current or even power factor. In addition to that, it also stores the energy profile that can be subsequently analysed. The device communicates locally via USB or remotely via Ethernet.
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GUI for REPOMO
Šimek, Petr ; Růžička, Richard (referee) ; Sekanina, Lukáš (advisor)
Today, reconfigurable hardware is happend tendency when we can configure static circuit structure in the same way as software. REPOMO is a reconfigurable chip which contains 32 polymorphic gates. Polymorphism means that a gate can change its logic function as a response to the power supply voltage. The purpose of this thesis is to introduce the area of polymorphic circuits and construction and function of REPOMO chip. The main goal of the thesis is proposed and implemeted graphical user interface which make easy progress applications for chip REPOMO.
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A Simple Digital Circuit Simulator
Kolman, Aleš ; Žaloudek, Luděk (referee) ; Sekanina, Lukáš (advisor)
Tituln. list esf This work is oriented on the simulation of digital circuits, especially small combinational and sequential circuits. This project is focused particularly on achieving the highest possible speed of simulation, for this reason was chosen programming language C. As Input format for this project was selected EDIF format of digital circuits. Output has not been specified.
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Accelerated Linear Genetic Programming in Hardware
Ťupa, Josef ; Bidlo, Michal (referee) ; Sekanina, Lukáš (advisor)
The aim of this thesis is to design and implement hardware acceleration of linear genetic programming for symbolic regression. The thesis contains a theoretical introduction into the studies of modern hardware and genetic programming design. Design and implementation of the LGP for symbolic regression is described in the rest of the thesis.
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Self-Modifying Programs in Cartesian Genetic Programming
Minařík, Miloš ; Slaný, Karel (referee) ; Sekanina, Lukáš (advisor)
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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Globally Controlled Cellular Automata
Švantner, Martin ; Žaloudek, Luděk (referee) ; Sekanina, Lukáš (advisor)
This master's thesis deals with cellular automata and further deals possibility of their global control. It describes the implementation of simulator of globally controlled cellular automata. The goal is, design and test the classification of globally controlled cellular automata. Classification is based on evaluation of influence of each parameter of automata on their dynamics.
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Artificial Immune Systems for Spam Detection
Hohn, Michal ; Sekanina, Lukáš (referee) ; Schwarz, Josef (advisor)
This work deals with creating a hybrid system based on the aggregation of artificial immune system with appropriate heuristics to make the most effective spam detection. This work describes the main principles of biological and artificial immune system and conventional techniques to detect spam including several classifiers. The developed system is tested using well known database corpuses and a comparison of the final experiments is made.
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Evolutionary Solving of the Rubik's Cube
Mališ, Radim ; Sekanina, Lukáš (referee) ; Jaroš, Jiří (advisor)
This thesis deals with an evolutionary solving of the Rubik's cube. The worldwide known puzzle has been for several decades not only a toy for children and adults, but also almost a lifestyle for crowds of fans and definitely a big challenge in the world of computation, where scientists seek to find an effective automated solution. The potential for its solution could also be borne by evolutionary algorithms. The author of this thesis has developed an application employing, apart from genetic algorithms, also many advanced technics, such as linear genetic programming or local search. The goal of this special technics is to make the evolutionary process more effective. There have also been made tests of the crossover, the population size and the mutation probability influence. All the tests have been statistically evaluated.
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Symbolic Regression and Coevolution
Drahošová, Michaela ; Žaloudek, Luděk (referee) ; Sekanina, Lukáš (advisor)
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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Multiobjective Cartesian Genetic Programming
Petrlík, Jiří ; Schwarz, Josef (referee) ; Sekanina, Lukáš (advisor)
The aim of this diploma thesis is to survey the area of multiobjective genetic algorithms and cartesian genetic programming. In detail the NSGAII algorithm and integration of multiobjective optimalization into cartesian genetic programming are described. The method of multiobjective CGP was tested on selected problems from the area of digital circuit design.
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