Proof Tree Kernels: a Candidate Ingredient for Intelligent Optimization
نویسندگان
چکیده
Reactive search techniques typically rely on the search history in order to adapt heuristics to the local conformation of the search space. By viewing search history as the trace of the optimization program, we aim to apply strategies for learning from example-traces, as developed in the fields of machine learning and inductive logic programming. We believe that Proof Tree Kernels, which we recently developed as a similarity measure between program traces, should provide a useful ingredient to fully exploit search histories. By retaining much of the structural information contained in traces, they can be employed to model the space conformation in order to appropriately adapt search heuristics or develop evaluation scores for candidate moves.
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