Advertisement

Epigenetically Inspired Modification of Genetic Algorithm and His Efficiency on Biological Sequence Alignment

  • Kornel Chromiński
  • Mariusz Boryczka
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 57)

Abstract

In this paper the modification of genetic algorithm inspired by the epigenetic process is presented. The results of the efficiency of the proposed modified algorithm are compared with standard genetic algorithm and a tool which does not use evolutionary processes.

Keywords

Genetic algorithm Epigenetics Sequence alignment 

References

  1. 1.
    Agarwal, P., Chauhan, R.: Alignment of multiple sequences using ga method. Int. J. Emerg. Technol. Comput. Appl. Sci. 4, 411–421 (2013)Google Scholar
  2. 2.
    Anbarasu, A., Narayanasamy, P., Sundararajan, V.: Multiple molecular sequence alignment by island parallel genetic algorithm. Curr. Sci. 78, 858–863 (2000)Google Scholar
  3. 3.
    Carey, N.: The Epigenetics Revolution: How Modern Biology is Rewriting Our Understanding of Genetics, Disease, and Inheritance. Columbia University Press (2013)Google Scholar
  4. 4.
    Goldberg, D.E.: Genetic Algorithms in Search. Scientific-Technical Publisher, Warsaw (2003). (in Polish)Google Scholar
  5. 5.
    Górny, A., Tkacz, M.A.: Using artificial neural networks for processing data gained via opendap and consolidated from different databases on distributed servers. In: Szczepaniak, P.S., Kacprzyk, J., Niewiadomski, A. (eds.) Advances in Web Intelligence Third International Atlantic Web Intelligence Conference, 2005. LNCS, vol. 3528, pp. 176–182. Springer (2005)Google Scholar
  6. 6.
    Gupta, R., Agarwal, P., Soni, A.: Genetic algorithm based approach for obtaining alignment of multiple sequences. Int. J. Adv. Comput. Sci. Appl. 3(12), 180–185 (2012)Google Scholar
  7. 7.
    Manning, T., Sleator, R., Walsh, P.: Naturally selecting solutions: the use of genetic algorithms in bioinformatics. Bioengineered 4(5), 266–278 (2013)CrossRefGoogle Scholar
  8. 8.
    Michalewicz, Z.: Genetic Algorithms \(+\) Data Structure \(=\) Evolutionary Program. Scientific-Technical Publisher, Warsaw (2004). (in Polish)Google Scholar
  9. 9.
    Radenbaugh, A.J.: Applications of Genetic Algorithms in Bioinformatics. San Jose State University, Master Thesis (2008)Google Scholar
  10. 10.
    Tkacz, M.: Artificial neural networks in incomplete data sets processing. In: Kłopotek, M.A., Wierzchoń, S.T., Trojanowski, K. (eds.) IIS: IIPWM’05, pp. 577–584. Advances in Soft Computing, Springer (2005)Google Scholar
  11. 11.
    Tkacz, M.: Artificial neural network resistance to incomplete data. In: Kłopotek, M.A., Wierzchoń, S.T., Trojanowski, K. (eds.) IIS: IIPWM’06, pp. 437–443. Advances in Soft Computing, Springer (2006)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 2.5 International License (http://creativecommons.org/licenses/by-nc/2.5/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

Authors and Affiliations

  1. 1.Institute of Technology and MechatronicsUniversity of SilesiaSosnowiecPoland
  2. 2.Institute of Computer ScienceUniversity of SilesiaSosnowiecPoland

Personalised recommendations