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Using artificial neural networks to control genetic algorithms
Dörfler, Martin ; Holeňa, Martin (advisor) ; Gemrot, Jakub (referee)
Genetic algorithms are some of the most flexible among optimization methods. Because of their low requirements on input data, they are able to solve a wide array of problems. The flexibility is balanced by their lower effectiveness. When compared to more specialized methods, their results are inferior. This thesis examines the possibility of increasing their effectiveness by means of controlling their run by an artificial neural network. Presented inside are means of controlling a run of a genetic algorithm by a self-organizing map. The thesis contains an algorithm proposal, a prototype implementation of such algorithm and a series of tests to assess its efficiency. While the results on benchmark functions show some positive properties, the problems of greater complexity yield less optimistic results.
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Anglicisms and their synonymic relations
Martinec, Karel ; Vachková, Marie (advisor) ; Šemelík, Martin (referee)
The thesis aims to analyze the selected anglicisms in terms of their synonymic relations with the corresponding German synonyms. By means of corpus analysis and the so called self- organizing maps (SOM) will be examined to what degree these pairs overlap or vary in their lexical meaning and what is the nature of their cooccurrence profiles and their stylistic marking. The appendix comprises some of 100 database entries.
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Anglicisms and their synonymic relations
Martinec, Karel ; Vachková, Marie (advisor) ; Šemelík, Martin (referee)
The thesis aims to analyze the selected anglicisms in terms of their synonymic relations with the corresponding German synonyms. By means of corpus analysis and the so called self- organizing maps (SOM) will be examined to what degree these pairs overlap or vary in their lexical meaning and what is the nature of their cooccurrence profiles and their stylistic marking. The appendix comprises some of 100 database entries.
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Using artificial neural networks to control genetic algorithms
Dörfler, Martin ; Holeňa, Martin (advisor) ; Gemrot, Jakub (referee)
Genetic algorithms are some of the most flexible among optimization methods. Because of their low requirements on input data, they are able to solve a wide array of problems. The flexibility is balanced by their lower effectiveness. When compared to more specialized methods, their results are inferior. This thesis examines the possibility of increasing their effectiveness by means of controlling their run by an artificial neural network. Presented inside are means of controlling a run of a genetic algorithm by a self-organizing map. The thesis contains an algorithm proposal, a prototype implementation of such algorithm and a series of tests to assess its efficiency. While the results on benchmark functions show some positive properties, the problems of greater complexity yield less optimistic results.
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