Inducing functions from examples is an important requirement in many
learning systems. Blind search is the most general approach, but is
vastly less efficient than specialized problem-solving methods. This
paper presents a new strategy to accelerate search without sacrificing
generality. Experiments with numeric functions show several orders of
magnitude performance increase over the standard search technique.
Two factors account for this improvement. First, the new strategy
manipulates functions in groups instead of singly, so that many can
be selected or discarded with only one comparison. Second, functional
equivalence is handled automatically by the internal organization
of search space.
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