Predictive Search Distributions
Edwin Bonilla, Christopher Williams, Felix Agakov, John Cavazos, John Thomson and Michael O'Boyle
In: ICML 2006, 25-29 June 2006, Pittsburgh, USA.
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.
|EPrint Type:||Conference or Workshop Item (Talk)|
|Project Keyword:||Project Keyword UNSPECIFIED|
|Subjects:||Theory & Algorithms|
|Deposited By:||Felix Agakov|
|Deposited On:||22 November 2006|