During the last years cartesian genetic programming proved to be a very perspective area of the evolutionary computing. However it has its limitations, which make its use in area of large and generic problems impossible. These limitations can be eliminated using the recent method allowing self-modification of programs in cartesian genetic programming. The purpose of this thesis is to review the development in this area done so far. Next objective is to design own solutions for solving various problems that are hardly solvable using the ordinary cartesian genetic programming. One of the problems to be considered is generating the terms of various Taylor series. Due to the fact that the solution to this problem requires generalisation, the goal is to prove that the self-modifying cartesian genetic programming scores better than classic one for this problem. Another discussed problem is using the self-modifying genetic programming for developing arbitrarily large sorting networks. In this case, the objective is to prove that self-modification brings new features to the cartesian genetic programming allowing the development of arbitrarily sized designs.

This master's thesis deals with evolutionary algorithms and how them to use to design of combinational circuits. Genetic programming especially CGP is the most applicable to use for this type of task. Furthermore, it deals with computation on computer cluster and the use of evolutionary algorithms on them. For this computation is the most suited island models with CGP. Then a new way of recombination in CGP is designed to improve them. This design is implemented and tested on the computer cluster.

This thesis deals with multi-stage stochastic linear programming and its ap- plictions in the portfolio selection problem. It presents several models of invest- ment planning, the emphasis is on the basic model with transaction costs and risk adjusted model for every investment level. Random returns entering the above models are modelled by the scenario trees which are generated using the moment- matching method. The thesis presents the optimal investment strategy for each model. It then examines distance of optimal values of objective functions in de- pendence on the nested distance of these generated trees. All calculations were performed using Mathematica software version 9. 1

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Diplomová práce je věnována vytvořené aplikaci DEAMS. DEAMS je webová aplikace pro řešení úloh modelů analýzy obalu dat a vyznačuje se uživatelskou přívětivostí, použitelností a jednoduchou prezentací a interpretací výsledků.

Diplomová práce zkoumá logistickou situaci v konkrétní firmě tak, aby optimalizace rozvozní úlohy nebyla jen dílčí optimalizací. Budoucí rozvoj firmy vede k navržení nových regionálních center na základě nejnovějších logistických trendů a zároveň se při rozhodování o umístění centra využije teorie diskrétního vícekriteriálního rozhodování. Samotné vybudování center vychází z teorii řízení projetků. Rozvozní úloha je aplikována na stávající i nová regionální centra s využitím aplikace v MS Excel a programu Lingo.

Mgr. Tomáš Ebenlendr Combinatorial algorithms for online problems: Semi-online scheduling on related machines Abstract of doctoral thesis We construct a framework that gives optimal algorithms for a whole class of scheduling problems. This class covers the most studied semi-online variants of preemptive online scheduling on uniformly related machines with the objective to minimize makespan. The algorithms from our framework are deterministic, yet they are optimal even among all randomized algorithms. In addition, they are optimal for any fixed combination of speeds of the machines, and thus our results subsume all the previous work on various special cases. We provide new lower bound of 2.112 for the original online problem. The (deterministic) upper bound is e ≈ 2.718 as there was known e-competitive randomized algorithm before. Our framework applies to all semi-online variants which are based on some knowledge about the input sequence. I.e., they are restrictions of the set of valid inputs. We use our framework to study restrictions that were studied before, and we derive some new bounds. Namely we study known sum of processing times, known maximal processing time, sorted (decreasing) jobs, tightly grouped processing times, approximately known optimal makespan and few combinations. Based on the analysis...

This thesis deals with designing a suitable methodology for the creation of educational computer games, based on an analysis of the current state of teaching computer games.

Havlík, M. Ant Colony Optimization: Implementation and testing of bio-inspired optimization method. Diploma thesis. Brno, 2015. This thesis deals with the implementation and testing of algorithm Ant Colony Optimization as a representative of the family of bio-inspired opti-mization methods. A given algorithm is described, analyzed and subsequently put into context with the problems which can be solved. Based on the collec-ted information is designed implementation that solves the Traveling sale-sman problem. Implementation contains graphical user interface to track the algorithm. Implementation is further optimized using parallel programming and other methods. Finally the implementation compared and summarized results.

Černý, M. Optimization plan of production and its effects on selected activities of company Granit spol. s r. o. Diploma thesis. Brno: Mendel University In Brno, 2015. The subject of this diploma thesis is to find the optimal plan of production of the company Granit spol. s r.o. using linear programming and subsequently finding the impact of introducing an optimized production structure on finance, ac-counting and marketing activities. For these purposes is created mathematical model, which is solved by Lindo program. Impacts on selected activities are ex-amined through changes in the values of financial indicators, balance sheet items and the ability to meet customers demand.