Statistical estimation in global random search algorithms in case of large dimensions

We study asymptotic properties of optimal statistical esti- mators 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

Stopping rules in k-adaptive global random search algorithms

Global random search, Global optimization, Extreme order statistics, Waiting times, k-th order statistic,