Algorithm Configuration: Learning policies for the quick termination of poor performers

26 Mar 2018Daniel KarapetyanAndrew J. ParkesThomas Stützle

One way to speed up the algorithm configuration task is to use short runs instead of long runs as much as possible, but without discarding the configurations that eventually do well on the long runs. We consider the problem of selecting the top performing configurations of the Conditional Markov Chain Search (CMCS), a general algorithm schema that includes, for examples, VNS... (read more)

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