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.
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Janusevskis, J., Le Riche, R., Ginsbourger, D., Girdziusas, R. (2012). Expected Improvements for the Asynchronous Parallel Global Optimization of Expensive Functions: Potentials and Challenges. In: Hamadi, Y., Schoenauer, M. (eds) Learning and Intelligent Optimization. LION 2012. Lecture Notes in Computer Science, vol 7219. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34413-8_37
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DOI: https://doi.org/10.1007/978-3-642-34413-8_37
Publisher Name: Springer, Berlin, Heidelberg
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