Learning Evaluation Functions for Global Optimization and Boolean Satisfiability

This paper describes STAGE, a learning approach to automatically improving search performance on optimization problems. STAGE learns an evaluation function which predicts the outcomeof a local search algorithm, such as hillclimbing or WALKSAT, as a function of state features along its search trajectories. Thelearned evaluation function is used to bias future search trajectories toward better optima. Wepresent positive results on six large-scale optimizationdomains.

Authors

• Justin A. Boyan Andrew W. Moore

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