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Coevolution of Image Filters and Fitness Predictors
Trefilík, Jakub ; Hrbáček, Radek (referee) ; Drahošová, Michaela (advisor)
This thesis deals with employing coevolutionary principles to the image filter design. Evolutionary algorithms are very advisable method for image filter design. Using coevolution, we can add the processes, which can accelerate the convergence by interactions of candidate filters population with population of fitness predictors. Fitness predictor is a small subset of the training set and it is used to approximate the fitness of the candidate solutions. In this thesis, indirect encoding is used for predictors evolution. This encoding represents a mathematical expression, which selects training vectors for candidate filters fitness prediction. This approach was experimentally evaluated in the task of image filters for various intensity of random impulse and salt and pepper noise design and the design of the edge detectors. It was shown, that this approach leads to adapting the number of target objective vectors for a particular task, which leads to computational complexity reduction.
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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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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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Hash Function Design Using Genetic Programming
Michalisko, Tomáš ; Piňos, Michal (referee) ; Sekanina, Lukáš (advisor)
This thesis deals with automated design of hash functions using Cartesian genetic programming. The chosen method for collision resolution is cuckoo hashing. Three variants of hash function encodings were compared. Experiments were performed with datasets containing network flows. The most suitable parameters of CGP, including the function set, were determined. The best evolved hash functions achieved comparable results to the functions designed by experts. The main finding is that hash functions consisting of 64-bit operations achieve the best results.
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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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Genetic Relatedness Analysis of Approximate Circuits
Krejčík, Vojtěch ; Mrázek, Vojtěch (referee) ; Sekanina, Lukáš (advisor)
The goal of this thesis is analyzing a large library of approximate circuits (EvoApproxLib) which was created using an evolutionary algorithm and used as a source of genetic data for the purposes of this thesis. More specifically it is a relatedness search in a file containing 24 912 8-bit approximate multipliers which were created by evolution from six different fully functioning parent implementations of multiplication. Gate counts and existence of 16 specific subcircuits were used as relatedness indicators. Various classifiers for assigning multipliers to one of six classes corresponding to parent implementations were trained based on these indicators. A classification success rate of up to 77% was achieved using said indicators. The results of this work show that combinations of specific subcircuits are a strong indicator for identifying which parent circuit the given approximate circuit comes from.
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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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Comparison of Genetic Programming Variants in the Symbolic Regression Task
Doležal, Petr ; Hurta, Martin (referee) ; Drahošová, Michaela (advisor)
This thesis deals with comparison of genetic programming variants it the task of symbolic regression. Time to converge and quality of evolved solutions are evaluated on nine chosen benchmarks. In particular, tree-based genetic programming, cartesian genetic programming and their modifications using coevolutionary algorithm are investigated. An own implementation of employed methods (without a specific library use) allows to share as much code as possible. Moreover, an analysis of implemented methods efficiency on real world data is provided. Experimental results show that all of the investigated approaches are capable of finding solutions using symbolic regression. Cartesian genetic programming enhanced with coevolution seems to be the most suitable of the investigated approaches in terms of evolved solution quality and time to converge.
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Modularity in the Evolutionary Design
Klemšová, Jarmila ; Bidlo, Michal (referee) ; Vašíček, Zdeněk (advisor)
The diploma thesis deals with the evolutionary algorithms and their application in the area of digital circuit design. In the first part, general principles of evolutionary algorithms are introduced. This part includes also the introduction of genetic algorithms and genetic programming. The next chapter describes the cartesian genetic programming and its modifications like embedded, self-modifying or multi-chromosome cartessian genetic programming. Essential part of this work consists of the design and implementation of a modularization technique for evolution circuit design. The proposed approach is evaluated using a set of standard benchmark circuits.
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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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