Algorithms for determining relative star height and star height
Information and Computation
SIAM Journal on Computing
Temporal difference learning and TD-Gammon
Communications of the ACM
Heuristics in Programming of Nondeterministic Games
Programming and Computing Software
Algorithmics for Hard Problems
Algorithmics for Hard Problems
Possible edges of a finite automaton defining a given regular language
The Korean Journal of Computational & Applied Mathematics
On the State Minimization of Nondeterministic Finite Automata
IEEE Transactions on Computers
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
On an expansion of nondeterministic finite automata
Journal of Applied Mathematics and Computing
Some competition programming problems as the beginning of artificial intelligence
Informatics in education
Extended Nondeterministic Finite Automata
Fundamenta Informaticae - Hardest Boolean Functions and O.B. Lupanov
Once More on the Edge-Minimization of Nondeterministic Finite Automata and the Connected Problems
Fundamenta Informaticae - Hardest Boolean Functions and O.B. Lupanov
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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, multiagent optimization, and neuronets.