Learning Evaluation Functions for Global Optimization and Boolean Satisfiability (1998)
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Abstract
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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Approximate BibTeX Entry
@inproceedings{boyan-learning,
Year = {1998},
Pages = {3-10},
Booktitle = {Proceedings of the Fifteenth National Conference on Artificial Intelligence},
Author = {
Justin Boyan, Andrew
Moore
},
Title = {Learning Evaluation Functions for Global Optimization and Boolean Satisfiability}
}