Variance Reduction for Better Sampling in Continuous Domains - Sorbonne Université
Communication Dans Un Congrès Année : 2020

Variance Reduction for Better Sampling in Continuous Domains

Résumé

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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Dates et versions

hal-02935395 , version 1 (10-09-2020)

Identifiants

Citer

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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