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

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.