Multiheuristic approach to discrete optimization problems

Melnikov, Boris

doi:10.1007/s10559-006-0070-y

Cited by 11 publications

(10 citation statements)

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“…On the contrary, results of parallel implementation of the seventh heuristic performed poor, and showed obviously wrong results for some DNA pairs. In [10] we had already covered an approach to parallelizing branch-andbouds algorithms, and here we used the same approach with heuristics. The results of that research contain lots of data and can't be presented here, so it will be described in a separate paper.…”

Section: Results Of Dna Comparisonmentioning

confidence: 99%

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On the Metaheuristics Approach to the Problem of Genetic Sequence Comparison and Its Parallel Implementation

Makarkin¹,

Melnikov²,

Panin³

2013

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We describe parallel implementation of the metaheuristic approach to the problem of comparing strings representing DNA sequence. By this approach, one can define a whole new class of metrics on a set of strings; some of this metrics can lead to interesting results when used for string comparison. We propose several heuristics; compare results achieved when using those heuristics and compare parallel and sequential implementation of proposed approach.

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Section: Results Of Dna Comparisonmentioning

confidence: 99%

“…Heuristics assessments are averaged using dynamical risk functions. Genetic algorithms are used to fit the averaging ratios, the same genetic algorithms with simplified self-learning are used for branch and bound startup ( [9,10]). …”

Section: Implementing Metaheuristic Approachmentioning

confidence: 99%

On the Metaheuristics Approach to the Problem of Genetic Sequence Comparison and Its Parallel Implementation

Makarkin¹,

Melnikov²,

Panin³

2013

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“…As we said before, the multiheuristic approach to the problems of discrete optimization was considered [8,9] and in many other following papers. In this section, we describe its version for determining the distance between nucleotide sequences.…”

Section: Algorithm For Determining the Distance Between Nucleotide Sementioning

confidence: 99%

“…There fore, if we really try to improve quality metrics, it is necessary to use a fundamentally different heuristics. For this thing, we were trying to find a function of the above type in which the set of values of the distance matrix, viewed as a distribution of a random variable, is obtained as close as possible to a uniform distribution 6 ; in advance, we note that for different tasks (i.e., for different concrete matrices of distances),the values of α, obtained by pseudo-optimal real-time algorithms (which are realized by algorithms of [8,9,14] etc.) are different.…”

Section: The Preprocessor Computing As a Special Normalization Of Datementioning

confidence: 99%

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Various algorithms, calculating distances of DNA sequences, and some computational recommendations for use such algorithms

Melnikov¹,

Pivneva²,

Trifonov³

2017

Collection of Selected Papers of the III International Conference on Information Technology and Nanotechnology

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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.

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