A steady state Genetic Algorithm for Multiple Sequence Alignment

Author

Pramanik, Sarah ; Setua, S.K.

Author_Institution

Dept. of Comput. Sci., Vidyasagar Univ., Midnapore, India

fYear

2014

fDate

24-27 Sept. 2014

Firstpage

1095

Lastpage

1099

Abstract

Multiple Sequence Alignment is one of the important research topics in Bioinformatics. The objective is to maximize the similarities among sequences by adding and shuffling gaps in sequences. We here present a genetic algorithm based approach to solve the problem efficiently. We use steady state Genetic Algorithm with a new form of chromosome representation. PAM 350 is used as scoring matrix for calculating the SOP score, which is the fitness score in genetic algorithm. The results are tested using BAliBASE benchmark dataset and it shows that the solution does offer better results.

Keywords

bioinformatics; computational complexity; genetic algorithms; matrix algebra; BAliBASE benchmark dataset; PAM 350; SOP score; bioinformatics; chromosome representation; fitness score; multiple sequence alignment; scoring matrix; steady state genetic algorithm; Biological cells; Dynamic programming; Evolutionary computation; Genetic algorithms; Sociology; Statistics; Steady-state; Bioinformatics; Computational Biology; Genetic Algorithm; Multiple Sequence Alignment; Steady state Genetic Algorithm;

fLanguage

English

Publisher

ieee

Conference_Titel

Advances in Computing, Communications and Informatics (ICACCI, 2014 International Conference on

Conference_Location

New Delhi

Print_ISBN

978-1-4799-3078-4

Type

conf

DOI

10.1109/ICACCI.2014.6968251

Filename

6968251