Variance Reduction for Better Sampling in Continuous Domains
Neural and Evolutionary Computing
2020-04-27 v1 Machine Learning
Machine Learning
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 and the dimension , and validate our results experimentally.
Cite
@article{arxiv.2004.11687,
title = {Variance Reduction for Better Sampling in Continuous Domains},
author = {Laurent Meunier and Carola Doerr and Jeremy Rapin and Olivier Teytaud},
journal= {arXiv preprint arXiv:2004.11687},
year = {2020}
}