In recent years, various approaches have been described to determine the similarity of the DNA sequences; each of which defines a metric for the set of DNA sequences. In this paper, we propose a new approach to solving this problem; moreover, algorithms for its implementation are based on multiheuristic approach to the discrete optimization problems previously developed by us. However, the main focus of this article is to describe our original approach to compare the quality of defined metrics on the set of DNA sequences. The last approach is based on the fact, that the triples of distances between genomes should ideally form isosceles acute triangles. On the basis of this assumption, we proposed value of the norm, gives in practice acceptable results; the validity of this approach is also discussed in the article. In the course of work on the implementation of algorithms have been carried out computational experiments with 100 DNA of "distant" species, as well as with representatives of several genomes of great apes and humans. Several possible standards defined comparative quality algorithms describing metric distances on DNA sequences. Thus, the main focus of this article is to describe our original approach to compare the quality of defined metrics on the set of DNA sequences. The approach is based on the fact, that the triples of distances between genomes should ideally form isosceles acute triangles. On the basis of this assumption, we proposed value of the norm, gives in practice aссeptable results. In the course of work on the implementation of algorithms have been carried out computational experiments with 100 DNA of "distant" species, as well as with representatives of several genomes of great apes and humans.
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.
The purpose of the article is to determine the essence and main characteristics of individual information technology learning systems automated based on information and communication technologies. The article defines the essence of automated individual learning systems based on information and communication technologies, which represent a technology that automatically adjusts educational content according to the actual level of educational achievements of the student, which this technology determines, as well as their individual characteristics (age, pace, psychotype, etc.). A comparative analysis of traditional learning systems and automated individual learning systems based on information and communication technologies has been carried out. The main advantages of individual learning systems have been characterized. The characteristics inherent in the vast majority of individual learning systems have been determined. The description of the main types of individual learning systems has been carried out. There are several indicators based on which, it is possible to determine whether a learning system is individual. It is noted that now, individual learning systems only begin active development and gradual introduction -such systems are not yet widespread even in the developed countries of the world, undergoing experimental testing. In the future, individual learning systems will become the engine of development of new pedagogy, strategies for the personification of education, and expansion of opportunities for active learning.
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.
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