  In this paper, we aim to compute numerical approximation integral by using an
adaptive Monte Carlo algorithm. We propose a stratified sampling algorithm
based on an iterative method which splits the strata following some quantities
called indicators which indicate where the variance takes relative big values.
The stratification method is based on the optimal allocation strategy in order
to decrease the variance from iteration to another. Numerical experiments show
and confirm the efficiency of our algorithm.
