Noisy kriging-based optimization methods: A unified implementation within the DiceOptim package

Victor Picheny and David Ginsbourger

Computational Statistics & Data Analysis, 2014, vol. 71, issue C, 1035-1053

Abstract: Kriging-based optimization relying on noisy evaluations of complex systems has recently motivated contributions from various research communities. Five strategies have been implemented in the DiceOptim package. The corresponding functions constitute a user-friendly tool for solving expensive noisy optimization problems in a sequential framework, while offering some flexibility for advanced users. Besides, the implementation is done in a unified environment, making this package a useful device for studying the relative performances of existing approaches depending on the experimental setup. An overview of the package structure and interface is provided, as well as a description of the strategies and some insight about the implementation challenges and the proposed solutions. The strategies are compared to some existing optimization packages on analytical test functions and show promising performances.

Keywords: Computer experiments; Gaussian processes; Active learning (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:71:y:2014:i:c:p:1035-1053

DOI: 10.1016/j.csda.2013.03.018

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