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	An Autonomous Driver of a TORCS Racing Car Běhal, Lukáš ; Kaštil, Jan (oponent) ; Jaroš, Jiří (vedoucí práce) This work describes the TORCS simulator and optimization algorithms used in the field of autonomous driving competitions. The main purpose of this work is to design a new controller solution based on genetic algorithms. The controller's behavior can be divided into two main parts which are exploited during the distinct stages of the competition. The warm-up stage serves for the track model sampling and the race line optimization. The race stage logic then benefits from the data obtained in the warm-up stage. The track optimization is done by a Genetic algorithm while the track is divided into several segments optimized separately. A genetic algorithm is applied once again to the track trajectory to smooth out gaps caused by the segment composition. In this work was shown that the track sampling and race line optimization by a genetic algorithm can be done during the warm-up stage. This makes the controller suitable for an autonomous driver competitions. Úplný záznam
	Overview of Nature-Inspired Optimization Algorithms Jendrálová, Martina ; Zbořil, František (oponent) ; Zbořil, František (vedoucí práce) The aim of this work was to investigate and compare the efficiency of four nature-inspired optimization algorithms in finding function extremes on various test functions. The algorithms included the cat swarm optimization algorithm, social-emotional optimization algorithm, dolphin echolocation algorithm, and harmony search algorithm. The chosen test functions for extreme searching were Rosenbrock function, Griewank function, and Rastrigin function. The work includes descriptions of individual experiments and evaluates the success of these algorithms in finding function extremes. Úplný záznam
	An Autonomous Driver of a TORCS Racing Car Běhal, Lukáš ; Kaštil, Jan (oponent) ; Jaroš, Jiří (vedoucí práce) This work describes the TORCS simulator and optimization algorithms used in the field of autonomous driving competitions. The main purpose of this work is to design a new controller solution based on genetic algorithms. The controller's behavior can be divided into two main parts which are exploited during the distinct stages of the competition. The warm-up stage serves for the track model sampling and the race line optimization. The race stage logic then benefits from the data obtained in the warm-up stage. The track optimization is done by a Genetic algorithm while the track is divided into several segments optimized separately. A genetic algorithm is applied once again to the track trajectory to smooth out gaps caused by the segment composition. In this work was shown that the track sampling and race line optimization by a genetic algorithm can be done during the warm-up stage. This makes the controller suitable for an autonomous driver competitions. Úplný záznam

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