A Hybrid Genetic Algorithm Using Dynamic Distance in Mutation Operator for Solving MSA Problem

  • Rohit Kumar YadavEmail author
  • Haider Banka
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9873)


In this paper, a hybrid Genetic Algorithm for solving multiple sequence alignment problems is proposed. Two new mechanisms have been introduced, i.e., one to generate the initial population and the second one is used during mutation operation. Here, the initial populations have been generated by Needleman Wunsch pair-wise alignment method. In the second step, the UPGMA method is used to generate the Guide tree with the help of two different matrix such as dynamic distance and edit distance matrix. The performance of the proposed method has been tested on publicly available benchmark datasets (i.e. Bali base) with some of the existing methods such as PRRP, CLUSTALX, SB−PIMA, MULTALIGN, SAGA, RBT-GA. We find that proposed method is better in most of cases and where it is not better at least close to best solution.


Multiple sequence alignment Genetic algorithm Pair-wise alignment Edit distance Dynamic distance 


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© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringIndian School of MinesDhanbadIndia

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