Search Results

You are looking at 1 - 4 of 4 items for :

  • Introductions and Overviews x
Clear All
Open access

Andrzej Pułka and Adam Milik

References GenBank (2010). [PUBMED] Pułka, A., Milik, A. (2008). A New Hardware Algorithm for Searching Genome Patterns. Proceedings of IEEE ICSES 2008 , Kraków, Poland, 177-180. Milik, A., Pułka, A. (2011). On Efficient Implementation of Search for Genome Patterns. PAK , 57(1), 15-18. (in Polish) Gusfield, D. (1997). Algorithms on strings, trees and sequences. Cambridge University Press . Smith, T. F., Waterman, M. S. (1981

Open access

W. Frohmberg, M. Kierzynka, J. Blazewicz, P. Gawron and P. Wojciechowski

References [1] J. Blazewicz, M. Bryja, M. Figlerowicz, P. Gawron, M. Kasprzak, E. Kirton, D. Platt, J. Przybytek, A. Swiercz, and L. Szajkowski, “Whole genome assembly from 454 sequencing output via modified DNA graph concept”, Comput. Biol. Chem. 33 (3), 224-230 (2009). [2] J. Blazewicz, W. Frohmberg, P. Gawron, M. Kasprzak, M. Kierzynka, A. Swiercz, and P.Wojciechowski, “DNA sequence assembly involving an acyclic graph model”, FCDS 38, 25-34, doi: 10.2478/v10209-011-0019-4 (2013). [3] Forge Genome

Open access

S. Grabowski and M. Raniszewski

. Chan, “Two efficient algorithms for linear time suffix array construction”, IEEE Trans. Comput. 60 (10), 1471–1484 (2011). [13] J. Kärkkäinen, “Suffix cactus: A cross between suffix tree and suffix array”, 6th Int. Symp. Combinatorial Pattern Matching, CPM 1995 , 191–204 (1995). [14] M. I. Abouelhoda, S. Kurtz, and E. Ohlebusch, “The enhanced suffix array and its applications to genome analysis”, 2nd Int. Workshop Algorithms in Bioinformatics, WABI 2002 , 449–463 (2002). [15] N. Grimsmo, On Performance and Cache Effects in Substring Indexes

Open access

K. Tajziehchi, A. Ghabussi and H. Alizadeh


In this paper, controlling and optimizing against the earthquake by using genetic algorithm is investigated. In this paper, a new approach for selecting optimal accelerograph and scaling them for dynamic time history analysis is presented by the binary genetic algorithm and natural numbers, in order to achieve the mean response spectrum, which has a proper matching and a short distance with the target spectrum and indicates the expected earthquake of the site. Because of the difference in the nature of accelerograph and the scale coefficients, the genetic algorithm presented in this paper, is hybrid (has two chromosomes). The proposed algorithm is capable of constructing a new generation of people from a series of infinitesimal earth movement records, in a process where natural selection, mating, mutation takes place, and creates a new generation of people and continues this process until a person with desirable qualities is obtained. One of the most important factors in the accuracy and efficiency of these programs is the correct estimation of their parameters. If these parameters are correctly calculated, the difference between the mean response spectrum and the spectrum of the plot will be greatly reduced. Due to the relatively large number of these parameters, the use of trial and error-based methods largely relies on user skills, the proposed hybrid genetic algorithm program can overcome this defect. The program has two genomes that run simultaneously and provide close answers to the optimal answer. The program itself is able to provide the user with a range of optimal coefficients and crossing values and mutations of each chromosome.