In the present work, we study possibilities of using artificial neural networks for accelerating of evolutionary algorithms. Improving consists in decreasing in number of calls to the fitness function, the evaluation of which is in some kinds of optimization problems very time- consuming and expensive. We use neural network as a regression model, which serves for fitness estimation in a run of evolutionary algorithm. Together with the regression model, we work also with the real fitness function, which we use for re-evaluation of individuals that are selecting according to a beforehand chosen strategy. These individuals re-evaluated by the real fitness function are used for improving the regression model. Because a significant number of individuals are evaluated only with the regression model, the number of calls to the real fitness function, that is needed for finding of a good solution of the optimization problem, is substantially reduced.

Title: Optimization and stress tests Author: Diana Fašungová Department: Department of Probability and Mathematical Statistics Supervisor: Prof. RNDr. Jitka Dupačová, DrSc., Department of Probability and Mathematical Statistics Abstract: In the thesis we apply contamination technique on a portfolio optimiza- tion problem using minimization of risk measure CVaR. The problem is considered from a risk manager point of view. We stress correlation structure of data and of revenues using appropriately chosen data for this kind of problem and for ge- nerated stress scenarios. From behaviour of CVaR with regard to contamination bounds, we formulate recommendations for the risk manager optimizing his port- folio. The recommendations are interpreted for both types of stress scenarios. In the end, limitations of the model and possible ways of improvement are discussed. Keywords: contamination bounds, stress tests, portfolio optimization, risk mana- gement

This thesis' topic is stochastic programming, in particular with regard to portfolio optimization and heavy tailed data. The first part of the thesis mentions the most common types of problems associated with stochastic programming. The second part focuses on solving the stochastic programming problems via the SAA method, especially on the condition of data with heavy tailed distributions. In the final part, the theory is applied to the portfolio optimization problem and the thesis concludes with a numerical study programmed in R based on data collected from Google Finance.

Robust optimization is a valuable alternative to stochastic programming, where all underlying probabilistic structures are replaced by the so-called uncertainty sets and all related conditions must be satisfied under all circumstances. This thesis reviews the fundamental aspects of robust optimization and discusses the most common types of problems as well as different choices of uncertainty sets. It focuses mainly on polyhedral and elliptical uncertainty and for the latter, in the case of linear, quadratic, semidefinite or discrete programming, computationally tractable equivalents are formulated. The final part of this thesis then deals with the well-known Flower-girl problem. First, using the principles of robust methodology, a basis for the construction of the robust counterpart is provided, then multiple versions of computationally tractable equivalents are formulated, tested and compared. Powered by TCPDF (www.tcpdf.org)

The main topic of this PhD thesis is metaheuristic algorithm in wider scope. The first chapters are dedicated to a description of broader context of metaheuristics, i.e. various optimization classes, determination of their omplexity and different approaches to their solutions. The consequent discussion about metaheuristics and their typical characteristics is followed by several selected examples of metaheuristics concepts. The observed characteristics serve as a base for building general metaheuristics model which is suitable for developing brand new or hybrid algorithms. The thesis is concluded by illustration of author’s publications with discussion about their adaptation to the proposed model. On the attached CD, there is also available a program implementation of the created model.

This work deals with swarm intelligence, strictly speaking particle swarm intelligence. It shortly describes questions of optimization and some optimization techniques. Part of this work is recherché of variants of particle swarm optimization algorithm. These algorithms are mathematically described. Their advantages or disadvantages in comparison with the basic PSO algorithm are mentioned. The second part of this work describes mQPSO algorithm and created modification mQPSOPC. Described algorithms are compared with each other and with another evolution algorithm on several tests.

Evolutionary algorithms have proved to be useful for tackling many practical black-box optimization problems. In this thesis, we describe one of the most powerful evolutionary algorithms of today, CMA- ES, and apply it in novel way to solve the problem of tuning multiple coupled PID controllers in combustion engine models. Powered by TCPDF (www.tcpdf.org)

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The aim of this work is the analysis of current practices and techniques of Internet marketing. The work is divided into two parts. The theoretical part presents the current internet marketing tools, the optimal utilization is reflected in the practical part, which is carried out a comprehensive plan for internet marketing strategy with emphasis on SEO for start-up company in the field of accommodation and hospitable service. This is a company Penzion U Lišky, which offers accommodation in Southern Bohemia. The work will serve as a guide for small and medium organizations in the development of marketing projects. The practical part of the evidence considered in developing websites and methods to optimize site for search engines. The next section provides recommendations for use of PPC campaigns and presentation of the company's social networking site Facebook. The analysis methods for increasing traffic through social networking, PR articles, blogging and other tools.

The Master's Thesis deals with an analysis of logistic system in the Unipetrol RPA Company. The objective of this thesis is to detect possible bottlenecks and to identify possibilities to optimize the logistic system. The first part of the Master's Thesis characterizes the terms logistics, Supply Chain Management, logistic chain, lean management, outsourcing, further it deals with the area of storage facilities, packaging, transportation, handling and storage means. The second chapter contains introduction of the Company, the respective branch of industry, as well as international project ChemLog. The analysis of the Company logistic system itself is described in the third chapter. Analyzed are individual logistic processes within the Company related to polyolefins (polyethylene and polypropylene). At the end of this chapter, bottlenecks of the system are detected and subsequently, certain recommendations proposed, which should be considered.