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Evolutionary Design of Combinational Circuits on Computer Cluster
Pánek, Richard ; Zachariášová, Marcela (referee) ; Hrbáček, Radek (advisor)
This master's thesis deals with evolutionary algorithms and how them to use to design of combinational circuits. Genetic programming especially CGP is the most applicable to use for this type of task. Furthermore, it deals with computation on computer cluster and the use of evolutionary algorithms on them. For this computation is the most suited island models with CGP. Then a new way of recombination in CGP is designed to improve them. This design is implemented and tested on the computer cluster.
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Evolutionary algorithms
Bortel, Martin ; Karásek, Jan (referee) ; Lambertová, Petra (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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A Tool for Visual Analysis of Circuit Evolution
Staurovská, Jana ; Minařík, Miloš (referee) ; Sekanina, Lukáš (advisor)
The main goal of the master's thesis is to compose a study on cartesian genetic programming with focus on evolution of circuits and to design a concept for visualisation of this evolution. Another goal is to create a program to visualise the circuit evolution in cartesian genetic programming, its generations and chromosomes. The program is capable of visualising the changes between generations and chromosomes and comparing more chromosomes at once. Several user cases had been prepared for the resulting program.
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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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Learnable Evolution Model for Optimization (LEM)
Grunt, Pavel ; Vašíček, Zdeněk (referee) ; Schwarz, Josef (advisor)
My thesis is dealing with the Learnable Evolution Model (LEM), a new evolutionary method of optimization, which employs a classification algorithm. The optimization process is guided by a characteristics of differences between groups of high and low performance solutions in the population. In this thesis I introduce new variants of LEM using classification algorithm AdaBoost or SVM. The qualities of proposed LEM variants were validated in a series of experiments in static and dynamic enviroment. The results have shown that the metod has better results with smaller group sizes. When compared to the Estimation of Distribution Algorithm, the LEM variants achieve comparable or better values faster. However, the LEM variant which combined the AdaBoost approach with the SVM approach had the best overall performance.
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Evolutionary Synthesis of Analog Electronic Circuits Using EDA Algorithms
Slezák, Josef ; Zaplatílek,, Karel (referee) ; Kolka, Zdeněk (referee) ; Dostál,, Tomáš (advisor)
Disertační práce je zaměřena na návrh analogových elektronických obvodů pomocí algoritmů s pravěpodobnostními modely (algoritmy EDA). Prezentované metody jsou na základě požadovaných charakteristik cílových obvodů schopny navrhnout jak parametry použitých komponent tak také jejich topologii zapojení. Tři různé metody využití EDA algoritmů jsou navrženy a otestovány na příkladech skutečných problémů z oblasti analogových elektronických obvodů. První metoda je určena pro návrh pasivních analogových obvodů a využívá algoritmus UMDA pro návrh jak topologie zapojení tak také hodnot parametrů použitých komponent. Metoda je použita pro návrh admitanční sítě s požadovanou vstupní impedancí pro účely chaotického oscilátoru. Druhá metoda je také určena pro návrh pasivních analogových obvodů a využívá hybridní přístup - UMDA pro návrh topologie a metodu lokální optimalizace pro návrh parametrů komponent. Třetí metoda umožňuje návrh analogových obvodů obsahujících také tranzistory. Metoda využívá hybridní přístup - EDA algoritmus pro syntézu topologie a metoda lokální optimalizace pro určení parametrů použitých komponent. Informace o topologii je v jednotlivých jedincích populace vyjádřena pomocí grafů a hypergrafů.
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Approximate Implementation of Arithmetic Operations in Image Filters
Válek, Matěj ; Vašíček, Zdeněk (referee) ; Sekanina, Lukáš (advisor)
Tato diplomová práce se zabývá aproximativní implementace aritmetických operací v obrazových filtrech. Zejména tedy využitím aproximativních technik pro úpravu způsobu násobení v netriviálním obrazovém filtru. K tomu je využito několik technik, jako použití převodu násobení s pohyblivou řadovou čárkou na násobení s pevnou řadovou čárkou, či využití evolučních algoritmů zejména kartézkého genetického programování pro vytvoření nových aproximovaných násobiček, které vykazují přijatelnou chybu, ale současně redukují výpočetní náročnost filtrace. Výsledkem jsou evolučně navržené aproximativní násobičky zohledňující distribuci dat v obrazovém filtru a jejich nasazení v obrazovém filtru a porovnání původního filtru s aproximovaným fitrem na sadě barevných obrázků.
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Co-Learning in Cartesian Genetic Programming
Korgo, Jakub ; Grochol, David (referee) ; Wiglasz, Michal (advisor)
This thesis deals with the integration of co-learning into cartesian genetic programming. The task of symbolic regression was already solved by cartesian genetic programming, but this method is not perfect yet. It is relatively slow and for certain tasks it tends not to find the desired result. However with co-learning we can enhance some of these attributes. In this project we introduce a genotype plasticity, which is based on Baldwins effect. This approach allows us to change the phenotype of an individual while generation is running. Co-learning algorithms were tested on five different symbolic regression tasks. The best enhancement delivered in experiments by co-learning was that the speed of finding a result was 15 times faster compared to the algorithm without co-learning.
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Solution of Continuous Systems by Evolutionary Computational Techniques
Lang, Stanislav ; Šeda, Miloš (referee) ; Olehla, Miroslav (referee) ; Matoušek, Radomil (advisor)
The thesis deals the issue of solution of continuous systems by evolutionary computational techniques. Evolutionary computing techniques fall into the field of softcomputing, an advanced metaheuristics optimization that is becoming more and more a method of solving complicated optimization problems with the gradual increase in computing performance of computers. The solution of continuous systems, or the synthesis of continuous control circuits, is one of the areas where these advanced algorithms find their application. When dealing with continuous systems we will focus on regulatory issues. Evolutionary computing can then become a tool not only for optimization of controller parameters but also to design its structure. Various algorithms (genetic algorithm, differential evolution, etc.) can be used to optimize the parameters of the controller, for the design of the controller structurewe usually encounter so called grammatical evolution. However, the use of grammatical evolution is not necessary if appropriate coding is used, as suggested in the presented thesis. The thesis presents a method of designing the structure and parameters of a general linear controller using the genetic algorithm. A general linear regulator is known also as so called polynomial controller, if we encounter the polynomial theory of control. The method of encoding the description of the general linear controller into the genetic chain is crucial, it determines a set of algorithms that are usable for optimization and influence the efficiency of the calculations. Described coding, effective EVT implementation, including multi-criteria optimization, is a key benefit of this work.
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