DNA-algorithm for timetable problem.

Q4 Health Professions

International Journal of Bioinformatics Research and Applications Pub Date : 2014-01-01 DOI:10.1504/IJBRA.2014.059520

Igor Yu Popov, Anastasiya V Vorobyova, Irina V Blinova

引用次数: 1

Abstract

Using of DNA molecules for solving of NP-complete problems is discussed. Properties of DNA allow one to reduce the number of operations from exponential to polynomial. DNA-algorithm for solving of the timetable problem is suggested. The starting point is a set of classes, teachers and hours with some limitations. It is necessary to determine whether there is a timetable satisfying all limitations. The sets of classes, teachers and hours are coded by chains of nucleotides. After preparing of the input multi-set containing all possible timetables the filtering procedure should be made. It allows to exclude all illegal timetables. The filtering algorithm is suggested. An example is described. The analysis of the algorithm is made.

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时间表问题的dna算法。

讨论了DNA分子在np完全问题中的应用。DNA的特性允许我们将运算次数从指数式减少到多项式式。提出了求解时间表问题的dna算法。起点是一系列的课程，老师和时间，有一些限制。有必要确定是否存在一个满足所有限制的时间表。课程、教师和课时的设置都是由核苷酸链编码的。在准备好包含所有可能时间表的输入多集后，应进行过滤程序。它允许排除所有非法的时间表。提出了滤波算法。给出了一个示例。对该算法进行了分析。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

International Journal of Bioinformatics Research and Applications Health Professions-Health Information Management

CiteScore

0.60

自引率

0.00%

发文量

期刊介绍： Bioinformatics is an interdisciplinary research field that combines biology, computer science, mathematics and statistics into a broad-based field that will have profound impacts on all fields of biology. The emphasis of IJBRA is on basic bioinformatics research methods, tool development, performance evaluation and their applications in biology. IJBRA addresses the most innovative developments, research issues and solutions in bioinformatics and computational biology and their applications. Topics covered include Databases, bio-grid, system biology Biomedical image processing, modelling and simulation Bio-ontology and data mining, DNA assembly, clustering, mapping Computational genomics/proteomics Silico technology: computational intelligence, high performance computing E-health, telemedicine Gene expression, microarrays, identification, annotation Genetic algorithms, fuzzy logic, neural networks, data visualisation Hidden Markov models, machine learning, support vector machines Molecular evolution, phylogeny, modelling, simulation, sequence analysis Parallel algorithms/architectures, computational structural biology Phylogeny reconstruction algorithms, physiome, protein structure prediction Sequence assembly, search, alignment Signalling/computational biomedical data engineering Simulated annealing, statistical analysis, stochastic grammars.