Many learning systems search through a space of possible performance elements, seeking an element with high expected utility. As the task of nding the globally optimal element is usually intractable, many practical learning systems use hill-climbing to nd a local optimum. Unfortunately, even this is diicult, as it depends on the distribution of problems, which is typically unknown. This paper a...