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.
| Identifer | oai:union.ndltd.org:nusl.cz/oai:invenio.nusl.cz:304112 |
| Date | January 2012 |
| Creators | Dörfler, Martin |
| Contributors | Holeňa, Martin, Gemrot, Jakub |
| Source Sets | Czech ETDs |
| Language | Czech |
| Detected Language | English |
| Type | info:eu-repo/semantics/masterThesis |
| Rights | info:eu-repo/semantics/restrictedAccess |
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