Learning Evaluation Functions for Global Optimization and Boolean Satisfiability
Conference Paper, Proceedings of 15th National Conference on Artificial Intelligence (AAAI '98), pp. 3 - 10, July, 1998
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.
Notes
(Selected as an AAAI-98 Outstanding Paper. Three of 475 submissions received this honor.)
(Selected as an AAAI-98 Outstanding Paper. Three of 475 submissions received this honor.)
BibTeX
@conference{Boyan-1998-16526,author = {Justin Boyan and Andrew Moore},
title = {Learning Evaluation Functions for Global Optimization and Boolean Satisfiability},
booktitle = {Proceedings of 15th National Conference on Artificial Intelligence (AAAI '98)},
year = {1998},
month = {July},
pages = {3 - 10},
}
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