National Repository of Grey Literature 12 records found  previous11 - 12  jump to record: Search took 0.00 seconds. 
Neural Networks and Genetic Algorithm
Karásek, Štěpán ; Snášelová, Petra (referee) ; Zbořil, František (advisor)
This thesis deals with evolutionary and genetic algorithms and the possible ways of combining them. The theoretical part of the thesis describes genetic algorithms and neural networks. In addition, the possible combinations and existing algorithms are presented. The practical part of this thesis describes the implementation of the algorithm NEAT and the experiments performed. A combination with differential evolution is proposed and tested. Lastly, NEAT is compared to the algorithms backpropagation (for feed-forward neural networks) and backpropagation through time (for recurrent neural networks), which are used for learning neural networks. Comparison is aimed at learning speed, network response quality and their dependence on network size.
Nature inspired search algorithms and their applications
Neruda, Roman
Basic principles of evolutionary algorithms and genetic search of parameter spaces are described in this paper. We explain the approaches common for genetic algorithms, evolutionary strategies, evolutionary programming, genetic programming, swarm algorithms, and neuroevolution. Published in proceedings Analýza dat 2013. Statistické metody pro technologii a výzkum. Pardubice : TriloByte Statistical Software, 2013, p. 69-80. ISSN 1805-6903. Presented as invited talk at the conference Analýza dat 2013.

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