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Acceleration of Particle Swarm Optimization Using GPUs
Krézek, Vladimír ; Schwarz, Josef (referee) ; Jaroš, Jiří (advisor)
This work deals with the PSO technique (Particle Swarm Optimization), which is capable to solve complex problems. This technique can be used for solving complex combinatorial problems (the traveling salesman problem, the tasks of knapsack), design of integrated circuits and antennas, in fields such as biomedicine, robotics, artificial intelligence or finance. Although the PSO algorithm is very efficient, the time required to seek out appropriate solutions for real problems often makes the task intractable. The goal of this work is to accelerate the execution time of this algorithm by the usage of Graphics processors (GPU), which offers higher computing potential while preserving the favorable price and size. The boolean satisfiability problem (SAT) was chosen to verify and benchmark the implementation. As the SAT problem belongs to the class of the NP-complete problems, any reduction of the solution time may broaden the class of tractable problems and bring us new interesting knowledge.
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Multiobjective Cartesian Genetic Programming
Petrlík, Jiří ; Schwarz, Josef (referee) ; Sekanina, Lukáš (advisor)
The aim of this diploma thesis is to survey the area of multiobjective genetic algorithms and cartesian genetic programming. In detail the NSGAII algorithm and integration of multiobjective optimalization into cartesian genetic programming are described. The method of multiobjective CGP was tested on selected problems from the area of digital circuit design.
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Evolutionary Design of Moving Objects
Fajkus, Jakub ; Schwarz, Josef (referee) ; Bidlo, Michal (advisor)
The aim of this work is to implement a system for automatic evolutionary design of virtual robot controllers. In particular, Linear Genetic Programming representation combined with a steady-state genetic algorithm will be used to find a suitable program that will lead a given virtual robot across a sequence of points denoting a predefined trajectory. The MuJoCo physics engine is applied to allow the user to specify the robot shape and to evaluate its behavior according to candidate programs generated by the genetic algorithm. The goal is to train the robot to follow the given path by optimizing the distance of the robot from the given points during the simulation. The optimization is performed by evolving the programs for a given number of generation of the genetic algorithm. Several sets of experiments will be presented and obtained results will be evaluated.
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Applied Artificial Immune Systems
Dolejší, Petr ; Bidlo, Michal (referee) ; Schwarz, Josef (advisor)
This final year thesis introduces the principles and properties of the artificial immune systems to the reader, then abstracts the principles from this knowledge and applies the real artificial immune systems on them. It provides a view at the practical applications that use and extend given ideas.
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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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Programming of Single-Chip Microcontroller
Jirka, Roman ; Strnadel, Josef (referee) ; Schwarz, Josef (advisor)
This bachelor's work deals with single-chip microcontrollers. The main purpose is to introduce the best known cores of universal microcontrollers to users, to introduce characteristics of individual cores, advantages, disadvantages and differences. The part of this work is a set of examples for microcontroller M68HC908LJ12 worked out in development environment CodeWarrior.
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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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