Advertisement

An Enhanced MapReduce Framework for Solving Protein Folding Problem Using a Parallel Genetic Algorithm

  • A. G. Hari Narayanan
  • U. Krishnakumar
  • M. V. Judy
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 248)

Abstract

Parallel Genetic algorithms have proved to be a successful method for solving the protein folding problem. In this paper we propose a simple genetic algorithm with optimum population size, mutation rate and selection strategy which is parallelized with MapReduce architecture for finding the optimal conformation of a protein using the two dimensional square HP model. We have used an enhanced framework for map Reduce which increased the performance of the private clouds in distributed environment. The proposed Genetic Algorithm was tested several bench mark of synthetic sequences. The result shows that GA converges to the optimum state faster than the traditional.

Keywords

Protein Folding Problem Hadoop MapReduce Parallel Genetic Algorithm 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Johnson, C.M., Katikireddy, A.: A Genetic Algorithm with Backtracking for Protein structure Prediction. In: GECCO 2006, Seatle, Washington, USA, July 8-12 (2006)Google Scholar
  2. 2.
    De Jong, K.A.: Analysis of the Behavior of a Class of Genetic Adaptive Systems. Ph.D. Dissertation, The University of Michigan, Ann Arbor, M1 (1975)Google Scholar
  3. 3.
    Dill, K.A.: Theory for the folding and stability of globular proteins. Biochemistry 24(6), 1501–1509 (1985)CrossRefGoogle Scholar
  4. 4.
    Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading (1989)MATHGoogle Scholar
  5. 5.
    Grefenstette, J., Gopal, R., Rosmaita, B., van Gucht, D.: Genetic Algorithms for the Traveling Salesman Problem. In: Proceedings of the 1st ICGA, pp. 160–168 (1985)Google Scholar
  6. 6.
    Haupt, R.L., Haupt, S.E.: Practical Genetic Algorithms. John Wiley & Sons, New York (1998)MATHGoogle Scholar
  7. 7.
    Metropolis, X., Rosenbluth, A.W., Rosenbluth, M.N., Teller, A.H., Teller, E.: Equation of state calculations by fast computing machines. J. Chem. P h p 21, 1087–1092 (1953)CrossRefGoogle Scholar
  8. 8.
    Unger, R., Moult, J.: A genetic algorithm for three dimensional protein folding simulations. In: Proceedings of the Fifth International Conference on Genetic Algorithms (ICGA 1993), Urbana-Champaign, IL, July 17-21, pp. 581–588. Morgan Kaufmann, San Francisco (1993)Google Scholar
  9. 9.
    Whitley, D.: The GENITOR Algorithm and Selection Pressure: Why Rank-Based Allocation of Reproductive Trials is Best. In: Proceedings of the 3rd International Conference on Genetic Algorithms ZCGA, 1989, pp. 116–121 (1989)Google Scholar
  10. 10.
    Martin, W.N., Lienig, J., Cohoon, J.P.: Population Structures Island (migration) models: evolutionary algorithms based on punctuated equilibria. In: Handbook of Evolutionary Computation (May 97 Release)Google Scholar
  11. 11.
    Di Geronimo, L., Ferrucci, F., Murolo, A., Sarro, F.A.: Parallel Genetic Algorithm Based on Hadoop MapReduce for the Automatic Generation of JUnit Test. In: 2012 IEEE Fifth International Conference on Proceedings of Software Testing, Verification and Validation (ICST) Issue Date: April 17-21, 2012Google Scholar
  12. 12.
    Wiese, K., Goodwin, S.D.: Parallel Genetic Algorithms for Constrained Ordering Problems. In: Proceedings of the 1lth International Florida Artificial Intelligence Research Symposium, FLAIRS 1998, pp. 101–105 (1998)Google Scholar
  13. 13.
    Judy, M.V., Ravichandran, K.S.: A solution to protein folding problem using a genetic algorithm with modified keep best reproduction strategy. In: IEEE Congress on Evolutionary Computation (2007), Library of Congress: 2007928155, pp. 4776–4780. IEEE Xplore (2007) ISBN: 1-4244-1340-0Google Scholar
  14. 14.
  15. 15.
    Apache Hadoop Map Reduce, http://hadoop.apache.org
  16. 16.
    Rajan, A., Judy, M.V.: An Enhanced Map Reduce Framework for Improving the Performance of Massively Scalable Private Clouds. In: International Journal of Computer Applications (IJCA), Proceedings on Amrita International Conference of Women in Computing, AICWIC 2013, January 24-26. Published by Foundation of Computer Science, New York (2013)Google Scholar
  17. 17.
  18. 18.
    Verma, A., Zea, N., Cho, B., Gupta, I., Campbell, R.H.: Breaking the Map Reduce stage Barriers. University of Illinois at Urbana-ChampaignGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • A. G. Hari Narayanan
    • 1
  • U. Krishnakumar
    • 1
  • M. V. Judy
    • 1
  1. 1.Department of Computer Science and ITAmrita School of Arts and Sciences, Amrita Vishwa VidyapeethamKochiIndia

Personalised recommendations