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One challenge in using artificial neural networks is how to determine appropriate parameters for network structure and learning. Often parameters such as learning rate or number of hidden units are set arbitrarily or with a general "intuition" as to what would be most effective. The goal of this project is to use a genetic algorithm to tune a population of neural networks to determine the best structure and parameters. This paper considers a genetic algorithm to tune the number of hidden units, learning rate, momentum, and number of examples viewed per weight update. Experiments and results are discussed for two domains with distinct properties, demonstrating the importance of careful tuning of network parameters and structure for best performance.



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