Discrete optimization problems - some new heuristic approaches

Melnikov, Boris

doi:10.1109/hpcasia.2005.34

Cited by 18 publications

(21 citation statements)

References 9 publications

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“…This problem is similar to the Set Covering Problem (SCP) which is in general NP-hard and in the worst cases methods for its solution behave like exhaustive search. To reduce the amount of time needed to find the appropriate (not necessary optimal) cover for large tables one can use various heuristic techniques, for example, genetic algorithm, simulated annealing or truncated branch and bound method (see [7]). The usage of such techniques on steps 2-4 of the above algorithm allows to obtain various heuristic algorithms based on the proposed approach.…”

Section: Inputmentioning

confidence: 99%

The State Minimization Problem for Nondeterministic Finite Automata: The Parallel Implementation of the Truncated Branch and Bound Method

Melnikov

Tsyganov

2012

2012 Fifth International Symposium on Parallel Architectures, Algorithms and Programming

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In this paper we present an approach to the parallel implementation of the state minimization problem for nondeterministic finite automata. This approach is based on the truncated branch and bound method and also on the usage of basis and COM automata for the given language. Minimum state automata are searched as sub-automata of the COM automaton. Some sufficient conditions for their equivalence to the given nondeterministic automaton are proved in terms of the loops of the basis automaton. We suggest exact and heuristic state minimization algorithms, discuss their implementation details and provide some experimental results.

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Section: Inputmentioning

confidence: 99%

The State Minimization Problem for Nondeterministic Finite Automata: The Parallel Implementation of the Truncated Branch and Bound Method

Melnikov

Tsyganov

2012

2012 Fifth International Symposium on Parallel Architectures, Algorithms and Programming

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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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“…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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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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Discrete optimization problems - some new heuristic approaches

Cited by 18 publications

References 9 publications

The State Minimization Problem for Nondeterministic Finite Automata: The Parallel Implementation of the Truncated Branch and Bound Method

The State Minimization Problem for Nondeterministic Finite Automata: The Parallel Implementation of the Truncated Branch and Bound Method

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

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

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