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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
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 language  English 

Title of host publication  ICML '06 Proceedings of the 23rd international conference on Machine learning 
Publisher  ACM 
Pages  121128 
Number of pages  8 
ISBN (Print)  1595933832 
DOIs  
Publication status  Published  2006 
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Dive into the research topics of 'Predictive Search Distributions'. Together they form a unique fingerprint.Projects
 2 Finished


PASCAL: Pattern analysis, statistical modelling and computational learning
1/12/03 → 30/09/08
Project: Research