Sequence Alignment Using Nature-Inspired Metaheuristic Algorithms

Muhammad Luthfi Shahab, Mohammad Isa Irawan


The most basic process in sequence analysis is sequence alignment, usually solved by dynamic programming Needleman-Wunsch algorithm. However, Needleman-Wunsch algorithm has some lack when the length of the sequence which is aligned is big enough. Because of that, sequence alignment is solved by metaheuristic algorithms. In the present, there are a lot of new metaheuristic algorithms based on natural behavior of some species, we usually call them as nature-inspired metaheuristic algorithms. Some of those algorithm that are more efficient are firefly algorithm, cuckoo search, and flower pollination algorithm. In this research, we use those algorithms to solve sequence alignment. The results show that those algorithms can be used to solve sequence alignment with good result and linear time computation.


Nature-inspired metaheuristic algorithms; sequence alignment

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A. Isaev, Introduction to mathematical methods in bioinformatics. Springer Science & Business Media, 2006.

M. Shahab, D. Utomo, and M. Irawan, “Decomposing and solving capacitated vehicle routing problem (CVRP) using two-step genetic algorithm (TSGA),” Journal of Theoretical and Applied Information Technology, vol. 87, no. 3, pp. 461–468, 2016.

X.-S. Yang, Nature-inspired optimization algorithms. Elsevier, 2014.

C. Notredame and D. Higgins, “Saga: sequence alignment by genetic algorithm,” Nucleic acids research, vol. 24, no. 8, pp. 1515–1524, 1996.

S. Shen, Theory and Mathematical methods in Bioinformatics. Springer Science & Business Media, 2008.



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