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International Conference on Swarm, Evolutionary, and Memetic Computing

SEMCCO 2015: Swarm, Evolutionary, and Memetic Computing pp 274–286Cite as

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

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9873))

Abstract

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.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Indian School of Mines, Dhanbad, 826004, India

    Rohit Kumar Yadav & Haider Banka

Authors
  1. Rohit Kumar Yadav
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  2. Haider Banka
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Corresponding author

Correspondence to Rohit Kumar Yadav .

Editor information

Editors and Affiliations

  1. IIT, New Dehli, India

    Bijaya Ketan Panigrahi

  2. Nanyang Technological University, Singapore, Singapore

    Ponnuthurai Nagaratnam Suganthan

  3. Indian Statistical Institute, Kolkata, India

    Swagatam Das

  4. Department of Computer Science Engineering, Anil Neerukonda Institute of Technology and Sciences, Visakhapatnam, India

    Suresh Chandra Satapathy

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Yadav, R.K., Banka, H. (2016). A Hybrid Genetic Algorithm Using Dynamic Distance in Mutation Operator for Solving MSA Problem. In: Panigrahi, B., Suganthan, P., Das, S., Satapathy, S. (eds) Swarm, Evolutionary, and Memetic Computing. SEMCCO 2015. Lecture Notes in Computer Science(), vol 9873. Springer, Cham. https://doi.org/10.1007/978-3-319-48959-9_24

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  • DOI: https://doi.org/10.1007/978-3-319-48959-9_24

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-48958-2

  • Online ISBN: 978-3-319-48959-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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