Discrete optimization problems - some new heuristic approaches

We consider in this paper some heuristic methods of decision-making in various discrete optimization problems. The object of each of these problems is programming anytime algorithms. Considered methods for solving these problems are constructed on the basis of special combination of some heuristics. We use some modifications of truncated branch-and-bound method; for the selecting immediate step, we apply dynamic risk functions; simultaneously for the selection of coefficients of the averaging-out, we use genetic algorithms; and the reductive self-learning by the same genetic methods is used for the start of truncated branch-and-bound method. This combination of heuristics represents a special approach to construction of anytime-algorithms for the discrete optimization problems, which is an alternative to the methods of linear programming, multi-agent optimization, and neuronets