About one forecast model of stochastic programming based on time series and genetic algorithms

We consider the problem of forecasting of complex objects' state, which characteristics are functions of time, that reduces to the solution of stochastic programming with probabilistic constraints. We suggest an approach in which stochastic programming is analyzed by time series and genetic algorithms.