SOGA: space oriented genetic algorithm for multiple sequence alignment

  • Authors

    • Ruchi Gupta
    • Pankaj Agarwal
    2018-09-22
    https://doi.org/10.14419/ijet.v7i4.5.21138
  • Genetic Algorithm, Crossover, Mutation, Selection, Multiple Sequence Alignment.

  • Abstract

    Multiple sequence alignment is one of the recurrent assignments in Bioinformatics. This method allows organizing a set of molecular sequences in order to expose their similarities and their differences. Although several applicable techniques were observed in this re- search, from traditional method such as dynamic programming to the extent of widely used stochastic optimization method such as Simu- lated Annealing and motif finding for solving this problem, their use is limited by the computing demands which are necessary for ex- ploring such a large and complex search space. This paper presents a new genetic algorithm, namely SOGA (Space Oriented Genetic Algorithm for Multiple Sequence Alignment), which has two new mechanisms: the first generates the population with randomly inserting the space between the selected sequences and the second applying new crossover and mutation operator, within an iterative process, to generate new and better solutions. This method is simple and fast. Its performance will further be tested on standard benchmark databas- es and will be compared with well-known algorithms. However, as its solutions clears that there is scope for further improvement.

     

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  • How to Cite

    Gupta, R., & Agarwal, P. (2018). SOGA: space oriented genetic algorithm for multiple sequence alignment. International Journal of Engineering & Technology, 7(4.5), 481-484. https://doi.org/10.14419/ijet.v7i4.5.21138

    Received date: 2018-10-06

    Accepted date: 2018-10-06

    Published date: 2018-09-22