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Variance Reduction for Better Sampling in Continuous Domains

Abstract : Design of experiments, random search, initialization of population-based methods, or sampling inside an epoch of an evolutionary algorithm use a sample drawn according to some probability distribution for approximating the location of an optimum. Recent papers have shown that the optimal search distribution, used for the sampling, might be more peaked around the center of the distribution than the prior distribution modelling our uncertainty about the location of the optimum. We confirm this statement, provide explicit values for this reshaping of the search distribution depending on the population size λ and the dimension d, and validate our results experimentally.
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https://hal.sorbonne-universite.fr/hal-02935395
Contributor : Carola Doerr <>
Submitted on : Thursday, September 10, 2020 - 12:07:00 PM
Last modification on : Tuesday, March 23, 2021 - 9:28:02 AM
Long-term archiving on: : Friday, December 4, 2020 - 5:20:38 PM

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Laurent Meunier, Carola Doerr, Jeremy Rapin, Olivier Teytaud. Variance Reduction for Better Sampling in Continuous Domains. Parallel Problem Solving from Nature – PPSN XVI (PPSN 2020), Sep 2020, Leiden, Netherlands. pp.154-168, ⟨10.1007/978-3-030-58112-1_11⟩. ⟨hal-02935395⟩

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