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Multi-Objective Partical Swarm Optimization
Benčok, Tomáš ; Žaloudek, Luděk (referee) ; Sekanina, Lukáš (advisor)
This bachelor work deals with the difficulties of multi-objective optimization of certain hard mathematical functions and non-traditional approaches for discovering their extremes. Main attention is given to the PSO (Particle Swarm Optimization) algorithm, implementation of which is also part of the bachelor work. The algorithm is modified in a such way, that it automatically builds Pareto-Optimal Set of the solutions. It's performance is compared with NSGA-II genetic algorithm on several test tasks.
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Temperature Measuring in Wellness
Gábriš, Michal ; Žaloudek, Luděk (referee) ; Bartoš, Pavel (advisor)
This Bachelor thesis deals with creation of operation program for digitally calibrated thermometer measuring two temperatures, which can be used in wellness centers. The thermometer operates in two modes, the mode of measuring and displaying of water temperature, and the mode of linear digital calibration, in which the temperature sensors are set. The Bachelor thesis contains both the description of used hardware components, and software solution implemented in assembly language. This device is already in commercial use.
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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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Evolution of Emergent Behavior in Cellular Systems
Novák, Radim ; Žaloudek, Luděk (referee) ; Bidlo, Michal (advisor)
This master's thesis deals with the topic of cellular automata and their utilization in the research of self-replication, especially with the focus on self-replicating loops. It also shows several possible approaches how to optimize the replication process. The first part is focused on theoretical aspects of cellular automata. It acquaints the readers with the questions of self-replication in the cellular automata and present some of the existing self-replicating loops, starting with the widely known Langton's loop. The second part presents the optimization of the replication process considering two selected variants of self-repricating loops - Byl's loop and Chou-Reggia loop. Two approaches are introduced together with their possible combination. The first approach is based on multiple self-replication. The second one is based on the reduction of the number of steps of the cellular automaton needed to create a copy of the loop.
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The Efficient Implementation of the Genetic Algorithm Using Multicore Processors
Kouřil, Miroslav ; Žaloudek, Luděk (referee) ; Jaroš, Jiří (advisor)
This diploma thesis deals with acceleration of advanced genetic algorithm. For implementation, discrete and continuos versions of UMDA genetic algorithm were chosen. The main part of the acceleration is the utilization of SSE instruction set. Using this set, the functions for calculating fitness and new population sampling were accelerated in particular. Then the pseudorandom number generator that also uses SSE instruction set was implemented. The discrete algorithm reached the speed of up to 4,6 after this implementation. Finally, the algorithms were modified so that the system OpenMP could be used, which enables the running of blocks of code in more threads. The continuous version of algorithm is not convenient for parallelization, because computational complexity of that algorithm is low. In comparison, the discrete versions of algorithm are really appropriate for parallelization. Both the implemented versions reached the total acceleration of up to 4,9 and 7,2.
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Evolutionary Design Using Random Boolean Networks
Mrnuštík, Michal ; Žaloudek, Luděk (referee) ; Bidlo, Michal (advisor)
This master's thesis introduces the Random Boolean Networks as a developmental model in the evolutionary design. The representation of the Random Boolean Networks is described. This representation is combined with an evolutionary algorithm. The genetic operators are described too. The Random Boolean Networks are used as the developmental model for the evolutionary design of the combinational circuits and the sorting networks. Moreover a representation of the Random Boolean Networks for the design of image filters is introduced. The proposed methods are evaluated in different case-studies. The results of the experiments are discussed together with the potential improvements and topics of the next research.
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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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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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Instruction-Controlled Cellular Automata
Bendl, Jaroslav ; Žaloudek, Luděk (referee) ; Bidlo, Michal (advisor)
The thesis focuses on a new concept of cellular automata control based on instructions. The instruction can be understood as a rule that checks the states of cells in pre-defined areas in the cellular neighbourhood. If a given condition is satisfied, the state of the central cell is changed according to the definition of the instruction. Because it's possible to perform more instructions in one computational step, their sequence can be understood as a form of a short program. This concept can be extended with simple operations applied to the instruction's prescription during interpretation of the instructions - an example of such operation can be row shift or column shift. An advantage of the instruction-based approach lies in the search space reduction. In comparison with the table-based approach, it isn't necessary to search all the possible configurations of the cellular neighbouhood, but only several areas determined by the instructions. While the groups of the inspected cells in the cellular neighbourhood are designed manually on the basis of the analysis of the solved task, their sequence in the chromosome is optimized by genetic algorithm. The capability of the proposed method of cellular automata control is studied on these benchmark tasks - majority, synchronization, self-organization and the design of combinational circuits.
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Evolutionary Circuit Design at the Transistor Level
Žaloudek, Luděk ; Vašíček, Zdeněk (referee) ; Sekanina, Lukáš (advisor)
This project deals with evolutionary design of electronic circuits with an emphasis on digital circuits. It describes the theoretical basics for the evolutionary design of circuits on computer systems, including the explanation of Genetic Programming and Evolutionary Strategies, possible design levels of electronic circuits, CMOS technology overview, also the overview of the most important evolutionary circuits design methods like development and Cartesian Genetic Programming. Next introduced is a new method of digital circuits design on the transistor level, which is based on CGP. Also a design system using this new method is introduced. Finally, the experiments performed with this system are described and evaluated.
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