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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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Prediction of Time Series Using Statistical Methods
Beluský, Ondrej ; Bidlo, Michal (referee) ; Schwarz, Josef (advisor)
Many companies consider essential to obtain forecast of time series of uncertain variables that influence their decisions and actions. Marketing includes a number of decisions that depend on a reliable forecast. Forecasts are based directly or indirectly on the information derived from historical data. This data may include different patterns - such as trend, horizontal pattern, and cyclical or seasonal pattern. Most methods are based on the recognition of these patterns, their projection into the future and thus create a forecast. Other approaches such as neural networks are black boxes, which uses learning.
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Artificial Intelligence Approaches for Filtering of Spams
Matula, Tomáš ; Žádník, Martin (referee) ; Schwarz, Josef (advisor)
This thesis focuses on the e-mail classification and describes the basic ways of spam filtering. The Bayesian spam classifiers and artificial immune systems are analyzed and applied in this thesis. Furthermore, existing applications and evaluation metrics are described. The aim of this thesis is to design and implement an algorithm for spam filtering. Ultimately, the results are compared with selected known methods.
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PSO-Particle Swarm Optimization
Němeček, Patrik ; Jaroš, Jiří (referee) ; Schwarz, Josef (advisor)
This work deals with particle swarm optimization. The theoretic part briefly describes the problem of optimization. The considerable part focuses on the overall description of particle swarm optimization (PSO). The principle, behavior, parameters, structure and modifications of PSO are described. The next part of the work is a recherché of variants of PSO, including hybridizations of PSO. In practical part the dynamic problems are analyzed and new designed algorithm for dynamic problems AHPSO is described (what it is based on, what was inspired, what elements are used and why). Algorithm is executed on the set of tasks (Moving peaks benchmark) and compared with the best publicly available variants of algorithm PSO on dynamic problems so far.
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Spam Filter Implementation on the Basis of Artificial Immune Systems
Neuwirth, David ; Schwarz, Josef (referee) ; Sekanina, Lukáš (advisor)
Unsolicited e-mails generally present a major problem within the e-mail communication nowadays. There exist several methods that can detect spam and distinguish it from the requested messages. The theoretical part of the masters thesis introduces the ways of detecting unsolicited messages by using artificial immune systems. It presents and subsequently analyses several methods of the artificial immune systems that can assist in the fight against spam. The practical part of the masters thesis deals with the implementation of a spam filter on the basis of the artificial immune systems. The project ends with comparison of effectiveness of the newly designed spam filter and the one which uses common methods for spam detection.
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PCB Layout on the OrCAD 16 Platform
Horčička, Martin ; Šimek, Václav (referee) ; Schwarz, Josef (advisor)
This bachelor's thesis concentrates on layout of printed wiring boards in OrCAD 16 environment, chiefly on specification of appropriate process methodology dutiny its proposal. A set of sample tasks that helps to explain and demonstrate the process at creation of separate phases is a part of this work. Among these phases belong electronical scheme designs,interconnection and subsequent simulation.
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Variation of the Evolutionary Algorithm for Dynamic Problems
Pokorný, Jan ; Bidlo, Michal (referee) ; Schwarz, Josef (advisor)
This study is focused on SOMA evolution algorithm and testing its versions aimed to solve dynamic problems. At the beginning it briefly explains principes of evolution algorithms and then it looks closer on SOMA algorithm. It describes its contemporary troubles for static and dynamic problems. There are also mentioned ways for their correction. It also describes the mostly used strategies -- All To One, All To Random, All To All and All To All Adaptive and shows their advantages and disadvantages. Furthermore another searching strategy is proposed focused on dynamic functions that are changing independently on program. The separate chapter is about project part of the study. There is described implementation and merging of used programs. This part is available on included CD along with results of testing strategies. Tables with average values are also part of the thesis.
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Learnable Evolution Model for Optimization (LEM)
Weiss, Martin ; Vašíček, Zdeněk (referee) ; Schwarz, Josef (advisor)
Numerical optimization of multimodal or otherwise nontrivial functions has stayed around the peak of the interest of many researchers for a long time. One of the promising methods that appeared is the hybrid approach of the Learnable Evolution Model that combines the well-established ways of artificial intelligence and machine learning with recently popular and efective methods of evolutionary programming. In this work, the method itself was reviewed with respect to what has been already implemented and tested and several possible new implementations of the method were proposed and some of them consequently implemented. The resulting program was then tested against a set of chosen nontrivial real-valued functions and its results were compared to those achieved with EDA algorithms.
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Artificial Immune Systems for Spam Detection
Hohn, Michal ; Sekanina, Lukáš (referee) ; Schwarz, Josef (advisor)
This work deals with creating a hybrid system based on the aggregation of artificial immune system with appropriate heuristics to make the most effective spam detection. This work describes the main principles of biological and artificial immune system and conventional techniques to detect spam including several classifiers. The developed system is tested using well known database corpuses and a comparison of the final experiments is made.
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Parallel Evolutionary Algorithm EDA Based on Copulas
Hyrš, Martin ; Brandejský, Tomáš (referee) ; Matoušek, Radomil (referee) ; Schwarz, Josef (advisor)
In my thesis I~ deal with the design, implementation and testing of the advanced parallel Estimation of Distribution Algorithm (EDA) utilizing copula theory to create a~ probabilistic model. A~new population is created by the process of sampling the joint distribution function, which models the current distribution of the subpopulation of promising individuals . The usage of copulas increases the efficiency of the learning process and sampling the probabilistic model. It can be separated into mutually independent marginal distributions and the copula , which represents the correlations between the variables of the solved problem. This concept initiated the usage of the parallel island architecture , in which the migration of probabilistic models belonging to individual islands ' subpopulations was used instead of the migration of individuals . The statistical tests used in the comparison of the proposed algorithm ( mCEDA = migrating Copula - based Estimation of Distribution Algorithm ) and the algorithms of other authors confirmed the effectiveness of the proposed concept .
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