Interval Estimation of Chance-Constrained Programming Based on Genetic Algorithm

The interval of the optimal value of the non-linear objective function of chance-constrained programmingwas estimated from the viewpoint of mathematical statistics, and the ways of improving the precision of the estimated interval were discussed. The genetic algorithm and the spline regression were adopted to obtain the Lipschitz constant of the fitted optimum objective function. A calculational example shows the validity of the interval estimation for the optimal value of a non-linear objective function.