Learning Evaluation Functions for Global Optimization and Boolean Satisfiability
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.
Cite
Text
Boyan and Moore. "Learning Evaluation Functions for Global Optimization and Boolean Satisfiability." AAAI Conference on Artificial Intelligence, 1998.Markdown
[Boyan and Moore. "Learning Evaluation Functions for Global Optimization and Boolean Satisfiability." AAAI Conference on Artificial Intelligence, 1998.](https://mlanthology.org/aaai/1998/boyan1998aaai-learning/)BibTeX
@inproceedings{boyan1998aaai-learning,
title = {{Learning Evaluation Functions for Global Optimization and Boolean Satisfiability}},
author = {Boyan, Justin A. and Moore, Andrew W.},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {1998},
pages = {3-10},
url = {https://mlanthology.org/aaai/1998/boyan1998aaai-learning/}
}