Predictive search distributions

Authors:
Edwin V. Bonilla;Christopher K. I. Williams;Felix V. Agakov;John Cavazos;John Thomson;Michael F. P. O'Boyle
Affiliations:
University of Edinburgh, Edinburgh, UK;University of Edinburgh, Edinburgh, UK;University of Edinburgh, Edinburgh, UK;University of Edinburgh, Edinburgh, UK;University of Edinburgh, Edinburgh, UK;University of Edinburgh, Edinburgh, UK
Venue:
ICML '06 Proceedings of the 23rd international conference on Machine learning
Year:
2006

Citing 6
Cited 1

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Abstract

Estimation of Distribution Algorithms (EDAs) are a popular approach to learn a probability distribution over the "good" solutions to a combinatorial optimization problem. Here we consider the case where there is a collection of such optimization problems with learned distributions, and where each problem can be characterized by some vector of features. Now we can define a machine learning problem to predict the distribution of good solutions q(s|x) for a new problem with features x, where s denotes a solution. This predictive distribution is then used to focus the search. We demonstrate the utility of our method on a compiler optimization task where the goal is to find a sequence of code transformations to make the code run fastest. Results on a set of 12 different benchmarks on two distinct architectures show that our approach consistently leads to significant improvements in performance.