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Application of Approximate Computing in Image Processing
Hruda, Petr ; Vašíček, Zdeněk (referee) ; Bidlo, Michal (advisor)
This master thesis focuses on approximate computing applied to image processing. Specifically, the approximation is applied to adaptive thresholding. Two approaches were used, the design of a new system using approximated components and the approximation of an existing algorithm. The resulting effect on thresholding quality was investigated. Experimental evaluation of the first approach shows quality improvements of thresholding with usage of aproximated components. Also, area of found aproximated solutions is smaller. Evaluation of the second approach shows worse quality of thresholding with usage of aproximated components. The second approach is then declared inappropriate.
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Comparison of Multi-objective Optimization Methods
Marek Martin
This paper deals with comparison of multi-objective optimization methods. Basic properties of multi-objective optimization are explained here. Algorithms NSGA-II, MOPSO and GDE3 are briefly introduced and compared using performance metrics on several test functions.
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Processes Supporting Decision-Making
Križan, Viliam ; Uher, Václav (referee) ; Karásek, Jan (advisor)
This thesis deals with algorithms for supporting decision processes. Firstly, Analytic Hierachy Process (AHP) and Analytic Network Process (ANP) developed by prof. Thomas L. Saaty are described. Basic principles and the implementation in Java programing language of the decision processes are explained. Both processes are then analyzed and their pros and cons and also practical aspect are explained. Secondly, the genetic algorithm NSGA-II developed by Kalyanmoy Deb is described. There are also basic principles and implementation in Java programing language explained. There are finaly the results of NSAG-II algorithm presented.
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Toolbox for multi-objective optimization
Marek, Martin ; Hurák,, Zdeněk (referee) ; Kadlec, Petr (advisor)
This paper deals with multi-objective optimization problems (MOOP). It is explained, what solutions in multi-objetive search space are optimal and how are optimal (non-dominated) solutions found in the set of feasible solutions. Afterwards, principles of NSGA-II, MOPSO and GDE3 algorithms are described. In the following chapters, benchmark metrics and problems are introduced. In the last part of this paper, all the three algorithms are compared based on several benchmark metrics.
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Implementation of an evolutionary expert system
Bukáček, Jan ; Müller, Jakub (referee) ; Karásek, Jan (advisor)
This thesis is focused on working up evolutionals and genetics algorithms issues Especially for multiobjective algorithms VEGA, SPEA and NSGA – II. Thereinafter one of FrameWork working with genetics algorithms namely WWW NIMBUS. From this mentioned algorithms was selected VEGA algorithm for implementation in JAVA to preselected problem. Thereby problem is choice thick columns of profile according to predetermined criteria. Selected algorithm works on division of population into several groups and each group evaluates the resulting fitness function. Here is a sample implementation of this algorithm. Furthermore there is a example of working with FrameWork. In the next section are compared the results of generated progam with results that were obtained by FrameWork WWW NIMBUS. As for VEGA, and the Nimbus there are shown different results. The VEGA is presented also the development of individual fitness functions. Also, there are shown graphs, that can be obtained from NIMBUS. At the end of work is introduced the comparation of the results ane propose possible improvements.
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