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Prediction of Secondary Structure of Proteins Using Cellular Automata
Brigant, Vladimír ; Drahošová, Michaela (referee) ; Bendl, Jaroslav (advisor)
This work describes a method of the secondary structure prediction of proteins based on cellular automaton (CA) model - CASSP. Optimal model and CA transition rule parameters are acquired by evolutionary algorithm. Prediction model uses only statistical characteristics of amino acids, so its prediction is fast. Achieved results was compared with results of other tools for this purpose. Prediction cooperation with a existing tool PSIPRED was also tested. It didn't succeed to beat this existing tool, but partial improvement was achieved in prediction of only alpha-helix secondary structure motif, what can be helful if we need the best prediction of alpha-helices. It was developed also a web interface of designed system.
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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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Cellular Automata Design Optimization
Jílek, Tomáš ; Drábek, Vladimír (referee) ; Bidlo, Michal (advisor)
Focus of this master's thesis is on evolutionary design of cellular automata and it's optimalization. There are described Evolutionary algorithms and Cellular automata in first part. Thereafter, one of the new ways of transition function representation and its possible evolutionary design is presented. Name of this method is Conditionally Matching Rules. This is followed by description of realized experiments with detailed results. Finally, success with optimalization for some tasks is presented in last chapter along with discussion.
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Genetic Programming for Design of Digital Circuits
Hejtmánek, Michal ; Bidlo, Michal (referee) ; Gajda, Zbyšek (advisor)
The goal of this work was the study of evolutionary algorithms and utilization of them for digital circuit design. Especially, a genetic programming and its different manipulation with building blocks is mentioned in contrast to a genetic algorithm. On the basis of this approach, I created and tested a hybrid method of electronic circuit design. This method uses spread schemes according to the genetic algorithm for the pattern problems witch are solved by the genetic programming. The method is more successful and have faster convergence to a solution in difficult electronic circuits design than a common algorithm of the genetic programming.
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Colearning in Coevolutionary Algorithms
Wiglasz, Michal ; Dobai, Roland (referee) ; Drahošová, Michaela (advisor)
Cartesian genetic programming (CGP) is a form of genetic programming where candidate programs are represented in the form of directed acyclic graphs. It was shown that CGP can be accelerated using coevolution with a population of fitness predictors which are used to estimate the quality of candidate solutions. The major disadvantage of the coevolutionary approach is the necessity of performing many time-consuming experiments to determine the best size of the fitness predictor for the particular task. This project introduces a new fitness predictor representation with phenotype plasticity, based on the principles of colearning in evolutionary algorithms. Phenotype plasticity allows to derive various phenotypes from the same genotype. This allows to adapt the size of the predictors to the current state of the evolution and difficulty of the solved problem. The proposed algorithm was implemented in the C language and optimized using SSE2 and AVX2 vector instructions. The experimental results show that the resulting image filters are comparable with standard CGP in terms of filtering quality. The average speedup is 8.6 compared to standard CGP. The speed is comparable to standard coevolutionary CGP but it is not necessary to experimentally determine the best size of the fitness predictor while applying coevolution to a new, unknown task.
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Aerodynamics Parametric Shape Aircraft Optimization
Dofek, Ivan ; Salga, Jaroslav (referee) ; Brož,, Václav (referee) ; Fiľakovský, Karol (advisor)
The work deals with the use of geometric parameterization for shape description of some parts of the airplane. Geometric parametrization is used for creating a parametric model airfoil. This parametric model allows local deformations pobrchu profile and can easily be applied to generate the geometry of the wing or other parts letoumu. Some properties of the parametric model were tested applications in aerodynamic optimization. Furthermore, the work deals with the parametric description of the blades, the aerodynamic optimization and noise analysis. For propeller blade were created distribution function of the control parameters that can be used in aerodynamic optimization of the blades. Geometric parameterization is used for identifying the location and other characteristics of noise sources.
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Evolutionary Synthesis of Analog Electronic Circuits Using EDA Algorithms
Slezák, Josef ; Zaplatílek,, Karel (referee) ; Kolka, Zdeněk (referee) ; Dostál,, Tomáš (advisor)
Disertační práce je zaměřena na návrh analogových elektronických obvodů pomocí algoritmů s pravěpodobnostními modely (algoritmy EDA). Prezentované metody jsou na základě požadovaných charakteristik cílových obvodů schopny navrhnout jak parametry použitých komponent tak také jejich topologii zapojení. Tři různé metody využití EDA algoritmů jsou navrženy a otestovány na příkladech skutečných problémů z oblasti analogových elektronických obvodů. První metoda je určena pro návrh pasivních analogových obvodů a využívá algoritmus UMDA pro návrh jak topologie zapojení tak také hodnot parametrů použitých komponent. Metoda je použita pro návrh admitanční sítě s požadovanou vstupní impedancí pro účely chaotického oscilátoru. Druhá metoda je také určena pro návrh pasivních analogových obvodů a využívá hybridní přístup - UMDA pro návrh topologie a metodu lokální optimalizace pro návrh parametrů komponent. Třetí metoda umožňuje návrh analogových obvodů obsahujících také tranzistory. Metoda využívá hybridní přístup - EDA algoritmus pro syntézu topologie a metoda lokální optimalizace pro určení parametrů použitých komponent. Informace o topologii je v jednotlivých jedincích populace vyjádřena pomocí grafů a hypergrafů.
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Job Scheduling in Logistic Warehouses
Povoda, Lukáš ; Uher, Václav (referee) ; Karásek, Jan (advisor)
The main aim of this thesis is flow shop and job shop scheduling problem in logistics warehouses. Managing and scheduling works is currently often problem. There is no simple solution due to complexity of this problem. This problem must be resolved because of a lack efficiency of work with a higher load such as during the christmas holidays. This paper describes the methods used to solve this problem focusing mainly on the use of search algorithms, evolutionary algorithms, specifically grammar guided genetic programming. This paper describes the problem of job shop scheduling on a simple theoretical example. The implemented algorithm for solving this problem was subjected to tests inspired on data from real warehouse, as well as synthetically created tests with more jobs and a greater number of workers. Synthetic tests were generated randomly. All tests were therefore run several times and the results were averaged. In conclusion of this work are presented the results of the algorithm and the optimum parameter settings for different sizes of problems and requirements for the solution. Genetic algorithm has been extended to calculate fitness of individuals with regard to number of collisions, extended to use priority rules during run of evolution, and some parts of algorithm was parallelized.
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Evolutionary algorithms
Bortel, Martin ; Karásek, Jan (referee) ; Burget, Radim (advisor)
Thesis describes main attributes and principles of Evolutionary and Genetic algorithms. Crossover, mutation and selection are described as well as termination options. There are examples of practical use of evolutionary and genetic algorithms. Optimization of distribution routes using PHP&MySQL and Google Maps API technologies.
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Solution of complex problems using evolutionary algorithms
Belovič, Boris ; Atassi, Hicham (referee) ; Burget, Radim (advisor)
Difficult problems are tasks which number of possible solutions increase exponentially or factorially. Application of common mathematical methods for finding proper solution in polynomial time is ineffective. Signal prediction is an example of diffucult problem. Signal is represented with a time serie and there is no explicit mathematical formula describing the signal. When genetic algorithms are applicated, they try to discover hidden patterns in time serie. These patterns can be used for prediction. Implication rules are used for discovery of these hidden patterns in time serie. Each rule is represented by one chromosome in population. Rules consist of two parts: conditional part and result part. Rules in population are compared with time serie and then the rules are evaluated according to their success in prediction. After the evaluation of rules, simulated evolution is started. Result of this evolution process is a group of rules which represent the most distinct patterns in time serie. These rules are then validated on validation set. Application is implemented in JAVA programming language.
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