(To appear in AAAI-98) Learning Evaluation Functions for Global Optimization and Boolean Satis ability

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.