This thesis presents the development of a design procedure for buckling-restrained braced frames (BRBF's). This procedure uses nonlinear time history analysis and a formal optimization algorithm. The time history analysis includes an elasto-plastic model for the braces. The optimization algorithm is a genetic algorithm. This procedure is referred to throughout the thesis as the "Nonlinear Time History Analysis Procedure with Optimization" (NTHO). Current design specifications for BRBF's are based on inelastic design spectra and approximate formulas for the determination of natural period. These spectra are used to obtain seismic base shear, and the distribution of equivalent lateral forces. Yielding and drift criteria are then used to determine brace areas. This design procedure is referred to throughout the thesis as the "Equivalent Lateral Force Procedure" (ELF). The thesis compares results from the NTHO and ELF procedures for a variety of BRBF's and levels of seismicity. The ELF procedure is judged against the more accurate NTHO procedure, and BRBF's are identified where the ELF procedure produces unconservative and excessively conservative designs. Since the NTHO procedure is more computationally expensive than the ELF procedure, design charts are developed for quickly sizing brace areas for a variety of BRBF's based on the NTHO procedure. Among the conclusions at the end of the thesis is the surprising result that the design charts show a near linear variation of brace area from story to story.

This work deals with the possibilities of employing a genetic algorithm to edge detection. There is introduced a project which uses enhanced image divided into sub-regions, on which detection by genetic algorithm is applied. To achieving our goals are used individuals in two-dimensional bit arrays, for which are specially adjusted mutation and crossover operators. Cost minimization approach is used as fitness function. The project was created and tested in Matlab environment.

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.

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.

This diploma thesis deals with genetic algorithms and their properties. Particular emphasis is placed on finding the influence of mutation and population size. Genetic algorithms are applied on inverse heat conduction problems (IHCP) in the second part of the thesis. Several different approaches and coding methods were tested. Properties of genetic algorithms were improved by definition of two new genetic operators – manipulation and sorting. Reported theoretical findings were tested on the real data of inverse heat conduction problem. The library for easy implementation of GA for solving general optimization problems in C ++ was created and is described in the last chapter.

The thesis deals with the acceleration of backpropagation neural networks using graphics chips. To solve this problem it was used the OpenCL technology that allows work with graphics chips from different manufacturers. The main goal was to accelerate the time-consuming learning process and classification process. The acceleration was achieved by training a large amount of neural networks simultaneously. The speed gain was used to find the best settings and topology of neural network for a given task using genetic algorithm.

This master thesis is focused on designing and implementing a software system, that is able to trade autonomously at stock market. Neural networks are used to predict future price. Genetic algorithm was used to find good combination of input parameters.

This thesis main focus is microscopic traffic sumulation. Part of this work is the design and implementation of microsimulation model based on cellular automaton. Implemented model supports calibration with genetic algorithm. The results of calibration and simulations are included.

The aim of this paper is to introduce the readers to the field of cellular automata, their design and their usage for structural design. Genetic algorithms are usually involved in designing complicated cellular automata, and because of that they are also briefly described here. For the purposes of this work sorting networks are considered as suitable structures to be designed using cellular automata, however, they are not a part of the automata but they are generated separately by modified rules of a local transition function.

Genetický algoritmus je optimalizačná metóda zameraná na efektívne hľadanie riešení rozličných problémov. Je založená na princípe evolúcie a prirodzeného výberu najschopnejších jedincov v prírode. Keďže je táto metóda výpočtovo náročná, bolo vymyslených veľa spôsobov na jej paralelizáciu. Avšak väčšina týchto metód je z historických dôvodov založená na superpočítačoch alebo rozsiahlych počítačových systémoch. Moderný vývoj v oblasti informačných technológií prináša na trh osobných počítačov stále lacnejšie a výkonnejšie viacjadrové systémy. Táto práca sa zaoberá návrhom nových metód paralelizácie genetického algoritmu, ktoré sa snažia naplno využiť možnosti práve týchto počítačových systémov. Tieto metódy sú následne naimplementované v programovacom jazyku C za využitia knižnice OpenMP určenej na paralelizáciu. Implementácia je následne použitá na experimentálne ohodnotenie rozličných charakteristík každej z prezentovaných metód (zrýchlenie oproti sekvenčnej verzii, závislosť konvergencie výsledných hodnôt od miery paralelizácie alebo od vyťaženia procesoru, ...). V poslednej časti práce sú prezentované porovnania nameraných hodnôt a závery vyplývajúce z týchto meraní. Následne sú prediskutované možné vylepšenia daných metód vyplývajúce z týchto záverov, ako aj možnosti spracovania väčšieho množstva charakteristík na presnejšie ohodnotenie efektivity paralelizácie genetických algoritmov.