This paper develops a variant of Simulated Annealing (SA) algorithm for solving discrete stochastic optimization problems where the objective function is stochastic and can be eva...
Statistical static timing analysis (SSTA) plays a key role in determining performance of the VLSI circuits implemented in state-of-the-art CMOS technology. A pre-requisite for emp...
A game-theoretic approach for learning optimal parameter values for probabilistic rough set regions is presented. The parameters can be used to define approximation regions in a p...
In multicriteria optimization, several objective functions, conflicting with each other, have to be minimized simultaneously. We propose a new efficient method for approximating t...
Stochastic perturbation methods can be applied to problems for which either the objective function is represented analytically, or the objective function is the result of a simula...