DOI

We study asymptotic properties of optimal statistical estimators in global random search algorithms when the dimension of the feasible domain is large. The results obtained can be helpful in deciding what sample size is required for achieving a given accuracy of estimation.

Original languageEnglish
Title of host publicationLearning and Intelligent Optimization - 11th International Conference, LION 11, Revised Selected Papers
EditorsDmitri E. Kvasov, Yaroslav D. Sergeyev, Roberto Battiti, Roberto Battiti, Dmitri E. Kvasov, Yaroslav D. Sergeyev
PublisherSpringer Nature
Pages364-369
Number of pages6
ISBN (Print)9783319694030
DOIs
StatePublished - 1 Jan 2017
Event11th International Conference on Learning and Intelligent Optimization, LION 2017 - Nizhny Novgorod, Russian Federation
Duration: 18 Jun 2017 → 20 Jun 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10556 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference11th International Conference on Learning and Intelligent Optimization, LION 2017
Country/TerritoryRussian Federation
CityNizhny Novgorod
Period18/06/17 → 20/06/17

    Research areas

  • Estimation of end-point, Extreme value, Global optimization, Random search

    Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

ID: 36692460