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We are working on the problem of developing a flexible, generic metal earning process that supports algorithm selection based on studying the algorithms' past performance behaviors. State of the art machine learning systems display limitations in that they require a great deal of human supervision to select an effective algorithm with corresponding options for a specific domain. Additionally, very...