
Coalition Formation in Multiagent Systems Using Genetic Algorithms
Kučera, Tomáš ; Uhlíř, Václav (referee) ; Zbořil, František (advisor)
This thesis discusses the basics of software agents and the way they form the multiagent coalitions. Genetic algorithms are introduced as one of the methods of solving the coalition formation problem. MAPC 2018 competition is introduced, which inspired the final design and implementation of the solution by using the tools described. A demo project was created, in which agents communicate with the MASSim server and gather data which is then used as an input into the genetic algorithm. Its purpose is to assign the agents to the tasks based on the input data, so that the tasks can be accomplished in the most effective manner possible. The results of this algorithm are evaluated in experiments which are focused on the quality of the solutions found as well as the time required for the calculation.


Optimization of Processes in Logistics with Visualization Support
Kršák, Martin ; Bidlo, Michal (referee) ; Křivka, Zbyněk (advisor)
The master thesis aims to design, implement, and compare algorithms that optimize processes in logistics, mainly in the planning phase. Heuristics and approximation genetic algorithms will find an nearoptimal solution to NPhard problem, such as the traveling salesman problem, with a delay less than several hours. The role of this algorithm is to plan an efficient route for garbage trucks that collect and distribute largescale waste to waste yards in a specific city. The goal of the optimization is to minimize the shipping costs.


Optimization by means of metaheuristics in Python using the DEAP library
Kesler, René ; Charvát, Pavel (referee) ; Klimeš, Lubomír (advisor)
{This thesis deals with optimization by means of metaheuristics, which are used for complicated engineering problems that cannot be solved by classical methods of mathematical programming. At the beginning, choosed metaheuristics are described: simulated annealing, particle swarm optimization and genetic algorithm; and then they are compared with use of test functions. These algorithms are implemented in Python programming language with use of package called DEAP, which is also described in this thesis. Algorithms are then applied for optimization of design parameters of the heat storage unit.


Evolution algorithms for ultrasound perfusion analysis
Kolářová, Jana ; Odstrčilík, Jan (referee) ; Mézl, Martin (advisor)
This master´s thesis is focused on the application of evolutionary algorithms for interleaving data obtained by ultrasound scanning of tissue. The interleaved curve serves to estimate perfusion parameters, thus allowing to detect possible pathophysiology in the scanned area. The theoretical introduction is devoted to perfusion and its parameters, contrast agents for ultrasonic application, ultrasonic modality scanning, optimization, evolutionary algorithms in general and two selected evolutionary algorithms  genetic algorithm and bee algorithm. These algorithms were tested on noisy data obtained from clinical images of mice with tumor. The final part summarizes the results of the practical part and provides suggestions and recommendations for further possible development.


Evolutionary Algorithms for Neural Networks Learning
Vosol, David ; Rozman, Jaroslav (referee) ; Zbořil, František (advisor)
Main point of this thesis is to find and compare posibilities of cooperation between evolutionary algorithms and neural network learning and their comparison with classical learning technique called backpropagation. This comparison is demonstrated with deep feedforward neural network which is used for classification tasks. The process of optimalization is via search of optimal values of weights and biases within neural network with fixed topology. We chose three evolutionary approaches. Genetic algorithm, differential evolution and particle swarm optimization algorithm. These three approaches are also compared between each other. The demonstrating program is implemented in Python3 programming language without usage of any third parties libraries focused on deep learning.


Algorithms for forward and backward planning
Sluka, Filip ; Hromková, Ivana (referee) ; Simeonov, Simeon (advisor)
The thesis deals with production planning. It contains theoretical description of methods used for production planning and optimizing. Thesis describes bottleneck problems in production. It offers overview of ways to identify and analyze bottleneck influence to manufacturing process efficiency. Thesis proposes ways to eliminate bottlenecks using various algorithm types. It applies theoretical knowledges from optimization and graph theory to program creation that is focused on order delay and readjustment time minimizing. The program implements genetic algorithm.


Browser Game with Artificial Intelligence
Moravec, Michal ; Volf, Tomáš (referee) ; Bartík, Vladimír (advisor)
Thesis describes design and implementation of a web browser game, which can be played by multiple players via the internet. The main goal is to manage the economy, although players can cooperate (trading) or play against each other (battles). NoSQL database is used for persistent storage of progress, which is also described in the thesis. Apart from human players there are also agents/bots, which play the game autonomously via state machines generated by genetic algorithms. Paper describes design and functionality of either the genetic algorithms, but also the state machines.


Hybrid Algorithms in Optimization
Zamazal, Petr ; Hrabec, Pavel (referee) ; Popela, Pavel (advisor)
This work deals with the solution of an integer programming task using a hybrid algorithm. Mentioned task is a minimum cost network flow problem with option of adding new edges. The hybrid algorithm is based on a genetic algorithm using the network simplex method. Implementation is in the Python programming language.


Logistic problems and their applications
Žlebek, Petr ; Nevrlý, Vlastimír (referee) ; Pavlas, Martin (advisor)
The thesis deals with transportation problems using optimization methods. The first part summarizes theoretical facts about the graph theory, mathematical programming, transportation problems and genetic algorithm. Those informations are used in the second part of the thesis where the specific transportation model dealing with waste collection management is described. The model is implemented in Matlab environment. Real data from the area of Czech republic are then applied to the model. Finally the results are throughly discussed.

 