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This master thesis explores the feasibility of using genetic algorithms in order to automate the process of optimizing hyperparameters for artificial neural networks (ANN). Today there is no standard way to optimize hyperparameters for ANN; often they are set manually by trial and error. In order to explore the feasibility of using genetic algorithms to optimize hyperparameters for ANN, two algori
