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Coevolution in Evolutionary Circuit Design
Veřmiřovský, Jakub ; Hrbáček, Radek (referee) ; Drahošová, Michaela (advisor)
This thesis deals with evolutionary design of the digital circuits performed by a cartesian genetic programing and optimization by a coevolution. Algorithm coevolves fitness predictors that are optimized for a population of candidate digital circuits. The thesis presents theoretical basis, especially genetic programming, coevolution in genetic programming, design of the digital circuits, and deals with possibilities of the utilization of the coevolution in the combinational circuit design. On the basis of this proposal, the application designing and optimizing logical circuits is implemented. Application functionality is verified in the five test tasks. The comparison between Cartesian genetic programming with and without coevolution is considered. Then logical circuits evolved using cartesian genetic programming with and without coevolution is compared with conventional design methods. Evolution using coevolution has reduced the number of evaluation of circuits during evolution in comparison with standard cartesian genetic programming without coevolution and in some cases is found solution with better parameters (i.e. less logical gates or less delay).
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Regession Methods in Traffic Prediction
Vaňák, Tomáš ; Korček, Pavol (referee) ; Petrlík, Jiří (advisor)
Master thesis deals with possibilities of predicting traffic situation on the macroscopic level using data, that were recorded using traffic sensors. This sensors could be loop detectors, radar detectors or cameras. The main problem discussed in this thesis is the travel time of cars. A method for travel time prediction was designed and implemented as a part of this thesis. Data from real traffic were used to test the designed method. The first objective of this thesis is to become familiar with the prediction methods that will be used. The main objective is to use the acquired knowledge to design and to implement an aplication that will predict required traffic variables.
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A Tool for Analysis of Digital Circuit Evolution Records
Kapusta, Vlastimil ; Bidlo, Michal (referee) ; Sekanina, Lukáš (advisor)
This master thesis describes stochastic optimization algorithms inspired in nature that use population of individuals - evolutionary algorithms. Genetic programming and its variant - cartesian genetic programming is described in a greater detail. This thesis is further focused on the analysis and visualization of digital circuit evolution records. Existing tools for visualization of the circuit evolution were analysed, but because no suitable tool allowing complex analysis of the circuit evolution was found, a new set of functions was proposed and the principles of a new tool were formulated. These functions were implemented in form of an interactive GUI application in Java programming language. The application was described in detail and then used for analysis of digital circuit evolution records.
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Utilization of Evolutionary Algorithms in Symbolic Regression Problem
Komadel, Michal ; Slaný, Karel (referee) ; Vašíček, Zdeněk (advisor)
Evolutionary algorithms are constantly developing and progressive part of informatics. These algorithms serve to solve many kinds of problems from optimal control to planning. This study discusses genetic and cartesian genetic programming, which belong among the most successful types of evolutionary algorithms. The goal of this work is to develop two aplications of genetic and cartesian genetic programming and evaluate efficiency of these two types of evolutionary algorithms in solving symbolic regression problems.
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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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Cartesian Genetic Programming in Evolutionary Art
Veselý, Pavel ; Hyrš, Martin (referee) ; Petrlík, Jiří (advisor)
This thesis deals with use of Cartesian Genetic Programming (CGP) in Evolutionary Art (EvoArt). Text presents introduction to the topic. The rest of the thesis focuses on the process of design, implementation and testing of new method of application CGP in EvoArt. The proposed method uses CGP to create 2D vector images. Web application for EvoArt creation is made to demonstrate this method. Achieved results are presented and evaluated.
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Evolutionary Design of Neural Networks with Generative Encoding
Hytychová, Tereza ; Bidlo, Michal (referee) ; Sekanina, Lukáš (advisor)
The aim of this work is to design and implement a method for the evolutionary design of neural networks with generative encoding. The proposed method is based on J. F. Miller's approach and uses a brain model that is gradually developed and which allows extraction of traditional neural networks. The development of the brain is controlled by programs created using cartesian genetic programming. The project was implemented in Python with the use of Numpy library. Experiments have shown that the proposed method is able to construct neural networks that achieve over 90 % accuracy on smaller datasets. The method is also able to develop neural networks capable of solving multiple problems at once while slightly reducing accuracy.
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Sorting Networks Design Using Coevolutionary CGP
Fábry, Marko ; Hrbáček, Radek (referee) ; Drahošová, Michaela (advisor)
This paper deals with sorting networks design using Cartesian Genetic Programming and coevolution. Sorting networks are abstract models capable of sorting lists of numbers. Advantage of sorting networks is that they are easily implemented in hardware, but their design is very complex. One of the unconventional and effective ways to design sorting networks is Cartesian Genetic Programming (CGP). CGP is one of evolutionary algorithms that are inspired by Darwinian theory of evolution. Efficiency of the CGP algorithm can be increased by using coevolution. Coevolution is an approach that works with multiple populations, which are influencing one another and constantly evolving, thus prevent the local optima deadlock. In this work it is shown, that with the use of coevolution, it is possible to achieve nearly twice the acceleration compared to evolutionary design.
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Evolutionary Design of Convolutional Neural Networks
Piňos, Michal ; Vašíček, Zdeněk (referee) ; Sekanina, Lukáš (advisor)
The aim of this work is to design and implement a program for automated design of convolutional neural networks (CNN) with the use of evolutionary computing techniques. From a practical point of view, this approach reduces the requirements for the human factor in the design of CNN architectures, and thus eliminates the tedious and laborious process of manual design. This work utilizes a special form of genetic programming, called Cartesian genetic programming, which uses a graph representation for candidate solution encoding.This technique enables the user to parameterize the CNN search process and focus on architectures, that are interesting from the view of used computational units, accuracy or number of parameters. The proposed approach was tested on the standardized CIFAR-10dataset, which is often used by researchers to compare the performance of their CNNs. The performed experiments showed, that this approach has both research and practical potential and the implemented program opens up new possibilities in automated CNN design.
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Evolutionary Optimization of Convolutional Neural Networks
Roreček, Pavel ; Mrázek, Vojtěch (referee) ; Sekanina, Lukáš (advisor)
This Master's Thesis is focused on the principles of neural networks, primarily convolutional neural networks (CNN). It introduces the evolutionary optimization in the context of neural networks. One of existing libraries devoted to the CNN design was chosen (Keras), analysed and used in image classification tasks. An optimization technique based on cartesian genetic programming that should reduce the complexity of CNN's computation was proposed and implemented. The impact of the proposed technique on CNN behaviour was evaluated in a case study.
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