Using Prediction to Improve Combinatorial Optimization Search

Abstract

This paper describes a statistical approach to improving the performance of stochastic search algorithms for optimization. Given a search algorithm $A$, we learn to predict the outcome of $A$ as a function of state features along a search trajectory. Predictions are made by a function approximator such as global or locally-weighted polynomial regression; training data is collected by Monte-Carlo simulation. Extrapolating from this data produces a new evaluation function which can bias future search trajectories toward better optima. Our implementation of this idea, STAGE, has produced very promising results on two large-scale domains.