Master thesis deals with possibilities of predicting traffic situation on the macroscopic level using data, that were recorded using traffic sensors. This sensors could be loop detectors, radar detectors or cameras. The main problem discussed in this thesis is the travel time of cars. A method for travel time prediction was designed and implemented as a part of this thesis. Data from real traffic were used to test the designed method. The first objective of this thesis is to become familiar with the prediction methods that will be used. The main objective is to use the acquired knowledge to design and to implement an aplication that will predict required traffic variables.

Genetic programming is a nature-inspired method of programming that allows an automated creation and adaptation of programs. For nearly two decades, this method has been able to provide human-comparable results across many fields. This work gives an introduction to the problems of evolutionary algorithms, genetic programming and the way they can be used to improve already existing software. This work then proposes a program able to use these methods to improve an implementation of cartesian genetic programming (CGP). This program is then tested on a CGP implementation created specifically for this project, and its functionality is then verified on other already existing implementations of CGP.

Stochastic methods are present in our daily lives, especially when we need to make a decision based on uncertain events. In this thesis, we present basic approaches used in stochastic tasks. In the first chapter, we define the stochastic problem and introduce basic methods and tasks which are present in the literature. In the second chapter, we present various problems which are non-linearly dependent on the probability measure. Moreover, we introduce deterministic and non-deterministic multicriteria tasks. In the third chapter, we give an insight on the concept of stochastic dominance and we describe the methods that are used in tasks with multidimensional stochastic dominance. In the fourth chapter, we capitalize on the knowledge from chapters two and three and we try to solve the role of portfolio optimization on real data using different approaches. 1

This thesis deals with stochastic location routing problem. Multiple stochas- tic and deterministic models are formulated and it is discussed that it is difficult to solve these problems via exact integer programming methods. It is necessary to develop heuristic methods to find a solution of these problems. Multiple ver- sions of these problems are formulated and their properties and possibilities how to solve them are discussed. Therefore, the brand new Blockchain metaheuristic is developed and later used for solving stochastic location routing problem ap- plied on a waste collection problem. As a part of Blockchain metaheuristic we develop the new application of Greedy algorihtm that is used for finding initial solution. The quality of the heuristic algorithm developed by us is presented in a numerical study. 1

This thesis describes the theory of model predictive control and application of the theory to synchronous drives. It shows explicit and on-line solutions and compares the results with classical vector control structure.

This thesis describes the theory of model predictive control and application of the theory to synchronous drives. It shows explicit and on-line solutions and compares the results with classical vector control structure.

Práce se zabývá určením pravděpodobnostních rozdělení pro stochastické programování, při kterém jsou optimální hodnoty účelové funkce extrémní (minimální nebo maximální). Rozdělení se určuje pomocí heuristických metod, konkrétně pomocí genetických algoritmů, kde celá populace aproximuje hledané rozdělení. První kapitoly popisují obecně matematické a stochastické programování a dále jsou popsány různé heuristické metody a s důrazem na genetické algoritmy. Těžiště práce je v naprogramování daného algoritmu a otestování na úlohách lineárních a kvadratických stochastických modelů.

This thesis concerns solutions to problems that arise in optimizing fixed interval scheduling under situations of uncertainty such as when there are random delays in job process times. These problems can be solved by using a vertex coloring with random edges and problems can be formulated using integer linear, quadratic and stochastic programming. In this thesis is propo- sed a new integer linear formulation. Under certain conditions there is proved its equivalence with stochastic formulation, where is maximized the schedule reliability. Moreover, we modified the proposed formulation to obtain bet- ter corresponding to real life situations. In a numerical study we compared computational time of individual formulations. It turns out that the propo- sed formulation is able to solve scheduling problems considerably faster than other formulations. 1

This thesis examines a conversion of a solution produced by geometric semantic genetic programming (GSGP) to an instantion of cartesian genetic programming (CGP). GSGP has proven its quality to create complex mathematical models; however, the size of these models can get problematically large. CGP, on the other hand, is able to reduce the size of given models. This thesis combinated these methods to create a subtree CGP (SCGP). The SCGP uses an output of GSGP as an input and the evolution is performed using the CGP. Experiments performed on four pharmacokinetic tasks have shown that the SCGP is able to reduce the solution size in every case. Overfitting was detected in one out of four test problems.

This thesis deals with image classification based on genetic programming and coevolution. Genetic programming algorithms make generating executable structures possible, which allows us to design solutions in form of programs. Using coevolution with the fitness prediction lowers the amount of time consumed by fitness evaluation and, therefore, also the execution time. The thesis describes a theoretical background of evolutionary algorithms and, in particular, cartesian genetic programming. We also describe coevolutionary algorithms properties and especially the proposed method for the image classifier evolution using coevolution of fitness predictors, where the objective is to find a good compromise between the classification accuracy, design time and classifier complexity. A part of the thesis is implementation of the proposed method, conducting the experiments and comparison of obtained results with other methods.