Expected improvements for the asynchronous parallel global optimization of expensive functions: potentials and challenges

Authors:
Janis Janusevskis;Rodolphe Le Riche;David Ginsbourger;Ramunas Girdziusas
Affiliations:
Ecole des Mines de Saint-Etienne, Saint-Etienne, France;Ecole des Mines de Saint-Etienne, Saint-Etienne, France,CNRS, UMR 5146 Cl., Goux, France;Departement of Mathematics and Statistics, University of Bern, Switzerland;Ecole des Mines de Saint-Etienne, Saint-Etienne, France
Venue:
LION'12 Proceedings of the 6th international conference on Learning and Intelligent Optimization
Year:
2012

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Abstract

Sequential sampling strategies based on Gaussian processes are now widely used for the optimization of problems involving costly simulations. But Gaussian processes can also generate parallel optimization strategies. We focus here on a new, parameter free, parallel expected improvement criterion for asynchronous optimization. An estimation of the criterion, which mixes Monte Carlo sampling and analytical bounds, is proposed. Logarithmic speed-ups are measured on 1 and 9 dimensional functions.