Learning Evaluation Functions to Improve Optimization by Local Search

Learning Evaluation Functions to Improve Optimization by Local Search (2000)

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by Justin Boyan , Andrew W. Moore , Pack Kaelbling

Venue:	Journal of Machine Learning Research
Citations:	49 - 0 self

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Datum	Value	Source
TITLE	Learning Evaluation Functions to Improve Optimization by Local Search	INFERENCE
AUTHOR NAME	Justin Boyan	user correction - Legacy Corrections
AUTHOR AFFIL	NASA Ames Research Center; Computational Sciences Division	user correction - Legacy Corrections
AUTHOR ADDR	Moett Field, CA 94035 USA	user correction - Legacy Corrections
AUTHOR NAME	Andrew W. Moore	user correction - Legacy Corrections
AUTHOR AFFIL	Carnegie Mellon University; Computer Science Department	user correction - Legacy Corrections
AUTHOR ADDR	Pittsburgh, PA 15213 USA	user correction - Legacy Corrections
AUTHOR NAME	Pack Kaelbling	user correction - Legacy Corrections
ABSTRACT	This paper describes algorithms that learn to improve search performance on largescale optimization tasks. The main algorithm, Stage, works by learning an evaluation function that predicts the outcome of a local search algorithm, such as hillclimbing or Walksat, from features of states visited during search. The learned evaluation function is then used to bias future search trajectories toward better optima on the same problem. Another algorithm, X-Stage, transfers previously learned evaluation functions to new, similar optimization problems. Empirical results are provided on seven large-scale optimization domains: bin-packing, channel routing, Bayesian network structure-finding, radiotherapy treatment planning, cartogram design, Boolean satisfiability, and Boggle board setup.	user correction - Legacy Corrections
YEAR	2000	INFERENCE
VENUE	Journal of Machine Learning Research	INFERENCE
VENUE TYPE	JOURNAL	INFERENCE
PAGES	2000	INFERENCE
VOLUME	1	INFERENCE
CITATIONS	51 found	ParsCit 1.0