This paper describes algorithms, corresponding computer programs and the results of computations, supplementing results published earlier. We consider the multiple sequence alignment problem, which can be nominated by a central problem in computational biology. For it, we continue to consider some different versions of so-called "triangular norm" defined on the set of triangles formed by the different distance between genomes computed by different algorithms. Besides, one of the problems considered in biocybernetics is the problem of reconstructing the distance matrix between DNA sequences, when not all the elements of the matrix under consideration are known at the input of the algorithm. In this connection, the problem arises that the developed method of comparative evaluation of algorithms for calculating the distances between sequences should be used for another problem, i.e., for reconstructing the matrix of distances between DNA sequences. In this paper, we consider the possibility of applying the method of comparative evaluation of the algorithms for calculating the distances between a pair of DNA strings that we developed and studied earlier for the reconstruction of a partially filled distance matrix. The restoration of the matrix occurs as a result of several computational passes. Estimates of unknown matrix elements are averaged in a special way using so-called risk functions, and the result of this averaging is considered as the received value of the unknown element.

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In this paper, we propose to examine the neural network approach to the problem of calculating the characteristics of the deformation and fracture of metals by transforming the fuzzy clustering of pulses of acoustic emission. To solve the problem of division the metal samples into two classes (deformed and non-deformed) we separated signals from the samples using two algorithms, i.e. fuzzy clustering and neural network. 2 clusters were always formed corresponding to 2 types of signals. The error of the neural network tests was within 5%. Thus, by this approach one can achieve sub-pixel sampling of acoustic data on clusters and neural network learning algorithm for the automatic calculation of the characteristics of metals.

We consider in this paper the adaptation of heuristics used for programming nondeterministic games to the problems of discrete optimization. In particular, we use some "game" heuristic methods of decision-making in various discrete optimization problems. The object of each of these problems is programming anytime algorithms. Among the problems described in this paper, there are the classical traveling salesman problem and some connected problems of minimization for nondeterministic finite automata. The first of the considered methods is the geometrical approach to some discrete optimization problems. For this approach, we define some special characteristics relating to some initial particular case of considered discrete optimization problem. For instance, one of such statistical characteristics for the traveling salesman problem is a significant development of the so-called "distance functions" up to the geometric variant such problem. And using this distance, we choose the corresponding specific algorithms for solving the problem. Besides, other considered methods for solving these problems are constructed on the basis of special combination of some heuristics, which belong to some different areas of the theory of artificial intelligence. More precisely, we shall use some modifications of unfinished branchand-bound method; for the selecting immediate step using some heuristics, we apply dynamic risk functions; simultaneously for the selection of coefficients of the averaging-out, we also use genetic algorithms; and the reductive self-learning by the same genetic methods is also used for the start of unfinished branch-and-bound method again. This combination of heuristics represents a special approach to construction of anytime-algorithms for the discrete optimization problems. This approach can be considered as an alternative to application of methods of linear programming, and to methods of multi-agent optimization, and also to neural networks.