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Crossover in Cartesian Genetic Programming
Vácha, Petr ; Vašíček, Zdeněk (referee) ; Sekanina, Lukáš (advisor)
Optimization of digital circuits still attracts much attention not only of researchers but mainly chip producers. One of new the methods for the optimization of digital circuits is cartesian genetic programming. This Master's thesis describes a new crossover operator and its implementation for cartesian genetic programming. Experimental evaluation was performed in the task of three-bit multiplier and five-bit parity circuit design.
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Mutation in Cartesian Genetic Programming
Končal, Ondřej ; Hrbáček, Radek (referee) ; Wiglasz, Michal (advisor)
This thesis examines various kinds of mutations in the Cartesian Genetic Programming (CGP) on tasks of symbolic regression. The CGP is kind of evolutionary algorithm which operates with executable structures. Programs in CGP are evolved using mutation, which leads to offspring evaluation, which is the most time-consuming part of the algorithm. Finding more suitable kind of mutation can significantly accelerate the creation of new individuals and thus, reduce the time necessary to find a satisfactory solution. This thesis presents four different mutations for CGP. Experiments compare these mutation operators to solve five tasks of symbolic regression. Experiments have shown that a choice of suitable mutation can almost double the computing speed in comparison to the standard mutation.
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Evolutionary Combinational Circuit Resynthesis
Pták, Ondřej ; Schwarz, Josef (referee) ; Sekanina, Lukáš (advisor)
This project deals with combinational digital circuits and their optimization. First there are presented main levels of abstraction utilized in the design of combinational digital circuits. Afterwards different methods are surveyed for optimization of combinational digital circuits. The next part of this project is mainly devoted to evolutionary algorithms, their common characteristics and branches: genetic algorithms, evolutionary strategies, evolutionary programming and genetic programming. The variant of genetic programming called Cartesian Genetic Programming (CGP) and the use of CGP in various areas, particularly in the synthesis and optimization of combinational logic circuits are described in detail. The project also discusses some modifications of CGP and the scalability problem of evolutionary circuit design. Consequential part of this thesis describes the method for evolution resynthesis of combinational digital circuits. There is description of design, especially the method of splitting circuits into subcircuits, and implementation details. Finally experiments with these method and their results are described.
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Coevolutionary Algorithm in FPGA
Hrbáček, Radek ; Vašíček, Zdeněk (referee) ; Drahošová, Michaela (advisor)
This thesis deals with the design of a hardware acceleration unit for digital image filter design using coevolutionary algorithms. The first part introduces reconfigurable logic device technology that the acceleration unit is based on. The theoretical part also briefly characterizes evolutionary and coevolutionary algorithms, their principles and applications. Traditional image filter designs are compared with the biologically inspired design methods. The hardware unit presented in this thesis exploits dual MicroBlaze system extended by custom peripherals to accelerate cartesian genetic programming. The coevolutionary image filter design is accelerated up to 58 times. The hardware platform functionality in the task of impulse noise filter design and edge detector design has been empirically analyzed.
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Coevolutionary Algorithms and Classification
Hurta, Martin ; Sekanina, Lukáš (referee) ; Drahošová, Michaela (advisor)
The aim of this work is to automatically design a program that is able to detect dyskinetic movement features in the measured patient's movement data. The program will be developed using Cartesian genetic programming equipped with coevolution of fitness predictors. This type of coevolution allows to speed up a design performed by Cartesian genetic programming by evaluating a quality of candidate solutions using only a part of training data. Evolved classifier achieves a performance (in terms of AUC) that is comparable with the existing solution while achieving threefold acceleration of the learning process compared to the variant without the fitness predictors, in average. Experiments with crossover methods for fitness predictors haven't shown a significant difference between investigated methods. However, interesting results were obtained while investigating integer data types that are more suitable for implementation in hardware. Using an unsigned eight-bit data type (uint8_t) we've achieved not only comparable classification performance (for significant dyskinesia AUC = 0.93 the same as for the existing solutions), with improved AUC for walking patient's data (AUC = 0.80, while existing solutions AUC = 0.73), but also nine times speedup of the design process compared to the approach without fitness predictors employing the float data type, in average.
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Evolutionary Design of Boolean Functions for Cryptography
Dvořák, Jan ; Vašíček, Zdeněk (referee) ; Husa, Jakub (advisor)
The goal of this bachelor's thesis is to compare various selection methods used in cartesian genetic programming applied to a problem of various types of cryptographically significant boolean functions. I focused on these selection methods: evolutionary strategies (1+lambda) and (1,lambda), tournament selection and roulette selection. The chosen problem was solved by an implementation of CGP with the above-mentioned selection methods and by a statistical evaluation of data acquired from conducted experiments. Evaluation of mentioned data has shown that the best results in case of bent functions were achieved while using (1+lambda) evolutionary strategy. The roulette selection performed the best in case of balanced functions with high nonlinearity.
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Design of S-Boxes Using Genetic Algorithms
Hovorka, Bedřich ; Zadina, Martin (referee) ; Hanáček, Petr (advisor)
This work deals with part of the encryption algorithm, called S-box and its development. For its development is used evolutionary computing, such as classical genetic algorithm, Estimation of Distribution Algorithm, Cartesian genetic programming and multi-criteria VEGA and SPEA algorithms. This thesis aims to test the properties of substitution boxes to its evolutionary development. Firstly, the work deals with cryptography and issues of s-boxes. There are explained basic concepts and describes the selected criteria of safety. Next chapter explains evolutionary algorithms and multi-criteria optimization. This knowledge is used to design and program implementation, which are described below. Finally discusses the application of the criteria studied. Discussed here is searching S-boxes in both single-criteria, and especially in multi-criteria genetic search.
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Coevolution of Fitness Predicotrs in Cartesian Genetic Programming
Drahošová, Michaela ; Pošík, Petr (referee) ; Šenkeřík, Roman (referee) ; Sekanina, Lukáš (advisor)
Kartézské genetické programován (CGP) je evoluc inspirovaná metoda strojového učen, která je primárně určená pro automatizovaný návrh programů a čslicových obvodů. CGP je úspěšné v řešen mnoha úloh z reálného světa. Avšak k nalezen inovativnch řešen obvykle potřebuje značný výpočetn výkon. Každý kandidátn program navržený pomoc CGP mus být spuštěn, aby se zjistilo, do jaké mry tento program řeš zadaný problém, a mohla mu být přiřazena fitness hodnota. Právě vyhodnocen fitness bývá výpočetně nejnáročnějš část návrhu pomoc CGP. Tato práce se zabývá využitm koevoluce prediktorů fitness v CGP za účelem zrychlen procesu evolučnho návrhu prováděného pomoc CGP. Prediktor fitness je malá podmnožina trénovacch dat použvaná pro rychlý odhad fitness hodnoty namsto náročného vyhodnocen objektivn fitness hodnoty. Koevoluce prediktorů fitness je optimalizačn metoda modelován fitness, která snižuje náročnost a frekvenci výpočtu fitness. V této práci je koevolučn algoritmus přizpůsoben pro CGP a jsou představeny a zkoumány tři přstupy k zakódován prediktorů fitness. Představená metoda je experimentálně vyhodnocena v pěti úlohách symbolické regrese a v úloze návrhu obrazových filtrů. Výsledky experimentů ukazuj, že pomoc této metody lze významně snžit výpočetn čas, který CGP potřebuje pro řešen zkoumané třdy úloh.
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Dynamic Approximation of Digital Circuits
Jásenský, Michal ; Hrbáček, Radek (referee) ; Sekanina, Lukáš (advisor)
This bachelor's thesis deals with design of a method based on cartesian genetic programming, which allows the evolutionary design of circuits capable of dynamic reconfiguration. The goal of reconfiguration is to dynamically change the number of used components and thereby to change the accuracy of calculation. In this thesis, implementation of the proposed method is described. The method is experimentally verified and demonstrated on several selected circuits.
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Coevolution of Image Filters and Noise Detectors
Komjáthy, Gergely ; Zachariášová, Marcela (referee) ; Drahošová, Michaela (advisor)
This thesis deals with image filter design using coevolutionary algorithms. It contains a description of evolutionary algorithms, focusing on genetic programming, cartesian genetic programming and coevolution, the reader can learn about image filters too. The next chapters contain the design of image filters and noise detectors using cooperative coevolution, and the implementation and testing of the proposed filter. In the last chapter the proposed filter is compared to other filters created using evolutionary algorithms but without coevolution.
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