CFP last date
20 January 2025
Call for Paper
February Edition
IJCA solicits high quality original research papers for the upcoming February edition of the journal. The last date of research paper submission is 20 January 2025

Submit your paper
Know more
The week's pick
Responsive image
SFLA-Based Line Balancing and Task Optimization in Master-Slave Controlled Hanger Transportation Systems for Garment Production

Duc Hoang Nguyen
Random Articles
Reseach Article

Prediction of Protein Structure using Parallel Genetic Algorithm

by Jasdeep Singh Bhalla, Anmol Aggarwal
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 81 - Number 11
Year of Publication: 2013
Authors: Jasdeep Singh Bhalla, Anmol Aggarwal
10.5120/14054-1781

Jasdeep Singh Bhalla, Anmol Aggarwal . Prediction of Protein Structure using Parallel Genetic Algorithm. International Journal of Computer Applications. 81, 11 ( November 2013), 7-11. DOI=10.5120/14054-1781

@article{ 10.5120/14054-1781,
author = { Jasdeep Singh Bhalla, Anmol Aggarwal },
title = { Prediction of Protein Structure using Parallel Genetic Algorithm },
journal = { International Journal of Computer Applications },
issue_date = { November 2013 },
volume = { 81 },
number = { 11 },
month = { November },
year = { 2013 },
issn = { 0975-8887 },
pages = { 7-11 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume81/number11/14054-1781/ },
doi = { 10.5120/14054-1781 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:55:46.554662+05:30
%A Jasdeep Singh Bhalla
%A Anmol Aggarwal
%T Prediction of Protein Structure using Parallel Genetic Algorithm
%J International Journal of Computer Applications
%@ 0975-8887
%V 81
%N 11
%P 7-11
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The Genetic Algorithms are generally used to draw a similarity between the Genetic mutation and Cross Over within populations from the field of biology. Genetic algorithms are highly and significantly parallel in nature and performance. These types of algorithms can be used to solve many other important problems such as the Graph Partitioning problem that deals with partitioning of graph, the famous Travelling salesman problems etc. Implementation of these algorithm shows a trade-off between Genetic search capable qualities and execution performance qualities. In this paper we worked in order to improvise the execution performance rate of algorithms, those particular implementations with lesser communications between populations are considered best and highly efficient. In this same direction, we tried to present an algorithm using discrete small subpopulation groups. Therefore, this particular implementation tries to reduce the quality of search of the algorithm. Thus, we tried to improve the quality of this type of search by having a centralized population system. Here, we analyzed some of the other alternatives for the implementation of these algorithms on distributed memory architectures in which centralized data can be significantly implemented. Prediction of tertiary protein structure is also presented in the paper as an example in which we tried to implement these alternatives of parallel algorithms on it. In the last section, we tried to summarize the performance analysis of the various proposed architectures.

References
  1. R. Tanese. Parallel Genetic Algorithm for a hypercube. in proc of 2nd int. conference on Genetic Algorithms and its applications,1987 Volkan Kurt, Protein Structure Prediction Using Decision Lists, 2005.
  2. ALFRED A. RABOW AND HAROLD A. SCHERAGA, Improved genetic algorithm for the protein folding problem by use of a Cartesian combination operator.
  3. Akshay Gupta, Sunil Kr. Singh, Khushboo Aggarwal, "Performance of Parallel Genetic Algorithms on Distributed Memory Architectures", Journal of Engineering Research and Studies, E-ISSN 0976-7916, JERS/Vol. II/ Issue I/January-March 2011/10-17.
  4. B. Manderick, P. Speissens, Fine Grained Parallel Genetic Algorithms, In proc of 3rd int. conference on Genetiv Algorithms and its applications pages 428-433,in 1989.
  5. Jane S Richardson, The Anatomy & Taxonomy of Protein Structure Joseph D. Szustakowski and Zhiping Weng, Protein Structure Alignment Using a Genetic Algorithm.
  6. Ricardo Bianchini, Christopher Brown, Parallel Genetic Algorithms on Distributed Memory Architecture.
  7. Richard Dallaway, Genetic programming and cognitive models.
  8. Ron Unger, The Genetic Algorithm Approach to Protein Structure Prediction Ron Unger, The Genetic Algorithm Approach to Protein Structure Prediction.
  9. J. Y Suh and D. V. Gutch. Distributed Genetic algorithms. Technical Report 225,Indiana University,Computer Science Department,1987 SHAOJIAN SUN, Reduced representation model ofprotein structure prediction Statistical potential and genetic algorithms.
  10. Sunil Kr. Singh, Khushboo Aggarwal, Akshay Gupta, In proc of Int. conference on Innovative Practices in Management & Information technology for Excellence at MAIMT, India, page 133-140.
  11. Steffen Schulze-Kremer, Genetic Algorithms for protein tertiary structure.
  12. S. Cahon, N. Melab, E. -G. Talbi, An enabling framework for parallel optimization on the computational grid, in: Proc. 5th IEEE/ACM Intl. Symposium onClusterComputing and the Grid,CCGRID'2005,Cardiff, UK, 9–12 May, 2005.
  13. E. -G. Talbi, A taxonomy of hybrid metaheuristics,Journal of Heuristics 8 (2002) 541–564.
  14. E. Alba, G. Luque, E. -G. Talbi, N. Melab, in: E. Alba (Ed. ), Metaheuristics and Parallelism,John Wiley and Sons, 2005.
  15. S. Cahon,N. Melab,E. -G. Talbi,ParadisEO:Aframework forthe reusable design of parallel and distributed metaheuristics,Journal of Heuristics 10 (2004) 357–380.
  16. B. Parent, A. Kok¨ osy, ¨ D. Horvath, Optimized evolutionary strategies in conformationalsampling,Journal of SoftComputing (2006).
  17. C. Levinthal, How to fold graciously, in: J. T. P. DeBrunner, E. Munck (Eds. ), Mossbauer Spectroscopy in Biological Systems (Proceedings of a Meeting Held at Allerton House, Monticello, Illinois), University of Illinois Press, 1969, pp. 22–24.
  18. J. D. Knowles, D. W. Corne, Reducing local optima in single-objective problems by multi-objectivization, in: E. Zitzler, et al. (Eds. ), Proc. First International Conference on Evolutionary Multi-criterion Optimization, EMO'01, Springer,Berlin, 2001, pp. 269–283.
  19. B. Ma, S. Kumar, C. -J. Tsai, R. Nussinov, Folding funnels and binding mechanisms, Protein Engineering 12, 713–720.
  20. J. J. Grefensttete, Parallel Adaptive Algorithms for function Optimization, Technical reportCS-81- 19, Nashville, Vanderbilt University.
Index Terms

Computer Science
Information Sciences

Keywords

Genetic Algorithms Protein Structure Prediction Parallel Genetic Algorithms Distributed Memory Architecture

Powered by PhDFocusTM