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Solving of Optimisation Tasks Inspired by Living Organisms
Popek, Miloš ; Peringer, Petr (referee) ; Martinek, David (advisor)
We meet with solving of optimization problems every day, when we try to do our tasks in the best way. An Ant Colony Optimization is an algorithm inspired by behavior of ants seeking a source of food. The Ant Colony Optimization is successfuly using on optimization tasks, on which is not possible to use a classical optimization methods. A Genetic Algorithm is inspired by transmision of a genetic information during crossover. The Genetic Algorithm is used for solving optimization tasks like the ACO algorithm. The result of my master's thesis is created simulator for solving choosen optimization tasks by the ACO algorithm and the Genetic Algorithm and a comparison of gained results on implemented tasks.
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Automatic Grouping of Regular Expressions
Stanek, Timotej ; Kořenek, Jan (referee) ; Kaštil, Jan (advisor)
This project is about security of computer networks using Intrusion Detection Systems. IDS contain rules for detection expressed with regular expressions, which are for detection represented by finite-state automata. The complexity of this detection with non-deterministic and deterministic finite-state automata is explained. This complexity can be reduced with help of regular expressions grouping. Grouping algorithm and approaches for speedup and improvement are introduced. One of the approches is Genetic algorithm, which can work real-time. Finally Random search algorithm for grouping of regular expressions is presented. Experiment results with these approches are shown and compared between each other.
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Cellular Automaton in Evolutionary Process
Hejč, Michal ; Herrman, Tomáš (referee) ; Bidlo, Michal (advisor)
The aim of this master's theses it to focuse on the usage of genetic algorithms in combination with a technique of biologically inspired development in cellular automata. The principles of the proposed method is described. The main part of this work deals with the design of combinational logic circuits. The genetic algorithm is utilized to design a nonuniform one-dimensional cellular automaton (in particular, the local transition functions) which serves as a circuit generator. Experiments have been conducted to design of basic types of combinational circuits and polymorphic circuits. Finally, the results are presented and compared with the results obtained in the previous work in which a uniform cellular automaton was applied.
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Ellipse Detection
Hříbek, Petr ; Herout, Adam (referee) ; Hradiš, Michal (advisor)
The thesis introduces methods used for an ellipse detection. Each method is theoretically described in current subsection. The description includes methods like Hough transform, Random Hough transform, RANSAC, Genetic Algorithm and improvements with optimalization. Further there are described modifications of current procedures in the thesis to reach better results. Next to the last chapter represents testing parameters of speed, quality and accuracy of implemented algorithms. There is a conclusion of testing and a result discussion at the end.
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Traveling Salesman Problem
Šůstek, Martin ; Snášelová, Petra (referee) ; Zbořil, František (advisor)
This thesis is focused on modification of known principles ACO and GA to increase their performance. Thesis includes two new principles to solve TSP. One of them can be used as an initial population generator. The appendix contains the implementation of the application in Java. The description of this application is also part of the thesis. One part is devoted to optimization in order to make methods more efficient and produce shorter paths. In the end of the thesis are described experiments and their results with different number of places from 101 up to 3891.
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The GPU Based Acceleration of Neural Networks
Šimíček, Ondřej ; Jaroš, Jiří (referee) ; Petrlík, Jiří (advisor)
The thesis deals with the acceleration of backpropagation neural networks using graphics chips. To solve this problem it was used the OpenCL technology that allows work with graphics chips from different manufacturers. The main goal was to accelerate the time-consuming learning process and classification process. The acceleration was achieved by training a large amount of neural networks simultaneously. The speed gain was used to find the best settings and topology of neural network for a given task using genetic algorithm.
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Meta-Heuristic Solution in RCPSP
Šebek, Petr ; Kočí, Radek (referee) ; Hrubý, Martin (advisor)
This thesis deals with the description of the state of resource-constrained project scheduling problem. It defines the formal problem and its complexity. It also describes variants of this problem. Algorithms for solving RCPSP are presented. Heuristic genetic algorithm GARTH is analyzed in depth. The implementation of prototypes solving RCPSP using GARTH is outlined. Several improvements to the original algorithm are designed and evaluated.
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System for Advanced Scheduling
Horký, Aleš ; Jaroš, Jiří (referee) ; Drahošová, Michaela (advisor)
This master thesis deals with the automatic design of examinations and courses scheduling. The design is adapted to the specific requirements of the Faculty of Information Technology of Brno University of Technology. A genetic algorithm and a heuristic algorithm are employed to solve this task. The genetic algorithm is used to specify the sequence of the examinations (or the courses) and then the heuristic algorithm spread them out into a timetable. An implementation (written in Python 3) provides a fast parallel processing calculation which can generate satisfactory schedules in tens of minutes. Performed experiments show approximately 13% better results in all considered criteria in comparison with utilized examination schedules in the past. The development was periodically consulted with persons responsible for the schedule processing at the faculty. The program will be used while designing of examination schedules for the academic year 2015/2016.
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Metrics and Criteria for Socio-Technical System Diagnostic
Raudenská, Lenka ; Dohnal, Mirko (referee) ; Nenadál, Jaroslav (referee) ; Fiala, Alois (advisor)
This doctoral thesis is focused on metrics and the criteria for socio-technical system diagnostics, which is a high profile topic for companies wanting to ensure the best in product quality. More and more customers are requiring suppliers to prove reliability in the production and supply quality of products according to given specifications. Consequently the ability to produce quality goods corresponding to customer requirements has become a fundamental condition in order to remain competitive. The thesis firstly lays out the basic strategies and rules which are prerequisite for a successful working company in order to ensure provision of quality goods at competitive costs. Next, methods and tools for planning are discussed. Planning is important in its impact on budget, time schedules, and necessary sourcing quantification. Risk analysis is also included to help define preventative actions, and reduce the probability of error and potential breakdown of the entire company. The next part of the thesis deals with optimisation problems, which are solved by Swarm based optimisation. Algorithms and their utilisation in industry are described, in particular the Vehicle routing problem and Travelling salesman problem, used as tools for solving specialist problems within manufacturing corporations. The final part of the thesis deals with Qualitative modelling, where solutions can be achieved with less exact quantitative information of the surveyed model. The text includes qualitative algebra descriptions, which discern only three possible values – positive, constant and negative, which are sufficient in the demonstration of trends. The results can also be conveniently represented using graph theory tools.
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Optimalization of Constructional Teams Creation by Genetic Algorithms
Špaček, Jiří ; Zelinka, Ivan (referee) ; Šeda, Miloš (referee) ; Ošmera, Pavel (advisor)
The thesis pertains to optimisation of workgroups in companies. It is based on the work of Dr. Meredith Belbin from the Henley Management College, who is the author of the so-called Belbin’s team role theory. The theory defines fundamental roles within a team including specifications of the behavioural patterns while stipulating that in order to ensure proper functionality of a team, it is essential for all the roles to be represented in it. However, in practice it is necessary for specific people to comply not only with certain personal and psychological requirements but also professional expertise and other requirements. Nevertheless, by the means of adding these parameters to specific people, an enormous number of possible alternatives of the resulting team, which may not be evaluated (easily and in the real time) using traditional methods, proves to come to existence. Therefore, the so-called genetic algorithms inspired by natural development processes originally described by J. G. Mendel and Ch. Darwin were selected for evaluation purposes. The genetic algorithms feature good solutions to the task to be resolved in a very short time while the task does not have to be based on exact specifications and therefore several solutions might exist. A Java application was created within the scope of the thesis; its core comprises a genetic algorithm and it was used for the purpose of modelling of specific teams. The results provided by the application were subsequently verified by the means of creation of teams used for completion of new tasks and monitoring their activities in practice. Furthermore, the model verification of teams previously created solely on the basis of experience of executives was performed and the respective results were compared.
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