Learning Evaluation Functions for Global Optimization and Boolean Satisfiability

Justin A. Boyan, Andrew W. Moore

This paper describes Stage , a learning approach to automatically improving search performance on optimization problems. Stage learns an evaluation function which predicts the outcome of a local search algorithm, such as hillclimbing or Walksat , as a function of state features along its search trajectories. The learned evaluation function is used to bias future search trajectories toward better optima. We present positive results on six large-scale optimization domains.


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