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 dene a machine learning problem to predict the distribution of good solutions q(sjx) 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 nd a sequence of code transformations to make the code run fastest. Results on a set of 12 dierent benchmarks on two distinct architectures show that our approach consistently leads to signicant improvements in performance.
Original languageEnglish
Title of host publicationICML '06 Proceedings of the 23rd international conference on Machine learning
PublisherACM
Pages121-128
Number of pages8
ISBN (Print)1-59593-383-2
DOIs
Publication statusPublished - 2006

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