Title :
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
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;
Conference_Titel :
Advances in Computing, Communications and Informatics (ICACCI, 2014 International Conference on
Conference_Location :
New Delhi
Print_ISBN :
978-1-4799-3078-4
DOI :
10.1109/ICACCI.2014.6968251
