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Neural Networks are powerful tools for classification and Regression, but it is difficult and time consuming to determine the best architecture for a given problem. In this paper two evolutionary algorithms, Genetic Algorithms (GA) and Binary Particle Swarm Optimization (BPS), are used to optimize the architecture of a Multi-Layer Perceptron Neural Network (MLP), in order to improve the predictive power of the credit risk scorecards. Results show that both methods outperform the Logisticdoi:10.1109/icdmw.2011.80 dblp:conf/icdm/BahnsenG11 fatcat:ihmfqe4ewzbvdg5levfsdwqioa