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)


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.


Protein Folding Problem Hadoop MapReduce Parallel Genetic Algorithm 


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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

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