Conventional genetic algorithms suffer from a dependence on the initial generation used by the algorithm. In case the generation consists of solutions which are not close enough to a global optimum but some of which are close to a relatively good local optimum, the algorithm is often guided a...

In this paper we consider stochastic discrete optimization problems (DOP) in which feasible solutions remain feasible irrespective of the randomness of the problem parameters. We introduce the concept of the risk associated with a solution and define optimal solution in terms of having least...

In a general class of discrete optimization problems with min-sum objective function, some of the elements may have random costs associated with them. In such a situation, the notion of optimality needs to be suitably modified. We define an optimal solution to be a feasible solution with the...