Expected Improvements for the Asynchronous Parallel Global Optimization of Expensive Functions : Potentials and Challenges

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 optimiza- tion 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.
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Article dans une revue
Lecture notes in computer science, springer, 2012, Learning and Intelligent Optimization, pp. 413-418. 〈10.1007/978-3-642-34413-8_37〉
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https://hal-emse.ccsd.cnrs.fr/emse-00686504
Contributeur : Florent Breuil <>
Soumis le : mardi 10 avril 2012 - 14:34:22
Dernière modification le : mardi 17 octobre 2017 - 12:08:01

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Janis Janusevskis, Rodolphe Le Riche, David Ginsbourger, Ramunas Girdziusas. Expected Improvements for the Asynchronous Parallel Global Optimization of Expensive Functions : Potentials and Challenges. Lecture notes in computer science, springer, 2012, Learning and Intelligent Optimization, pp. 413-418. 〈10.1007/978-3-642-34413-8_37〉. 〈emse-00686504〉

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