We consider noisy optimization and some traditional variance reduction techniques aimed at improving the convergence rate, namely (i) common random numbers (CRN), which is relevant for population-based noisy optimization and (ii) stratified sampling, which is relevant for most noisy optimization problems. We present artificial models of noise for which common random numbers are very efficient, and artificial models of noise for which common random numbers are detrimental. We then experiment on a desperately expensive unit commitment problem. As expected, stratified sampling is never detrimental. Nonetheless, in practice, common random numbers provided, by far, most of the improvement.
CITATION STYLE
Christophe, J. J., Decock, J., Liu, J., & Teytaud, O. (2016). Variance reduction in population-based optimization: Application to unit commitment. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9554, pp. 219–233). Springer Verlag. https://doi.org/10.1007/978-3-319-31471-6_17
Mendeley helps you to discover research relevant for your work.