Neural networks are one of the most efficient classifier methods. One of such classifying neural networks we are trying to teach in this work by using genetic algorithms. In this work we test two types of genetic algorithms. One may be called parameterized genetic algorithm. It is built on the basic ideas of genetic algorithms. The other one is called parameter less genetic algorithm. It was presented by F. G. Lobo and D. E. Goldberg. Both genetic algorithms are tested and compared to the other well known optimization methods such as Bayes and Monte Carlo search. Experiments show the relevance of use genetic algorithms in teaching classifying neural network. Also stated that parameter less genetic algorithm works more efficient than parametric genetic algorithm in general cases. Created programs will be used in future studies.
| Identifer | oai:union.ndltd.org:LABT_ETD/oai:elaba.lt:LT-eLABa-0001:E.02~2004~D_20040528_155652-64676 |
| Date | 28 May 2004 |
| Creators | Balnys, Mantas |
| Contributors | Plėštys, Rimantas, Barauskas, Rimantas, Palubeckis, Gintaras, Pranevičius, Henrikas, Jasinevičius, Raimundas, Telksnys, Laimutis, Matickas, Jonas Kazimieras, Kanapeckas, Pranas, Mockus, Jonas, Kaunas University of Technology |
| Publisher | Lithuanian Academic Libraries Network (LABT), Kaunas University of Technology |
| Source Sets | Lithuanian ETD submission system |
| Language | Lithuanian |
| Detected Language | English |
| Type | Master thesis |
| Format | application/pdf |
| Source | http://vddb.library.lt/obj/LT-eLABa-0001:E.02~2004~D_20040528_155652-64676 |
| Rights | Unrestricted |
Page generated in 0.0042 seconds