[PDF][PDF] Learning evaluation functions for global optimization and boolean satisfiability
JA Boyan, AW Moore�- AAAI/IAAI, 1998 - cdn.aaai.org
JA Boyan, AW Moore
AAAI/IAAI, 1998•cdn.aaai.orgThis 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.
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.
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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