Survey of the use of genetic algorithm for multiple sequence alignment

Mohamed Tahar Ben Othman

Article Details

Survey of the use of genetic algorithm for multiple sequence alignment

Authors:

Mohamed Tahar Ben Othman

Publisher:

Science Publishing Corporation

Journal:

Journal of Advanced Computer Science & Technology Vol.5 Issue 2, pp.28-33

Document Type:

Article

Publication Date:

8/2016

Document Language:

English

Document ID (Org No.):

2001388483

pISSN:

2227-4332

Keywords:

Genetic Algorithms

Abstract:

 Multiple Sequence Alignment (MSA) is used in genomic analysis, such as the identification of conserved sequence motifs, the estimation of evolutionary divergence between sequences, and the genesâ€™ historical relationships inference. Several researches were conducted to determine the level of similarity of a set of sequences. Due to the problem of the NP-complete class property, a number of researches use genetic algorithms (GA) to find a solution to the multiple sequence alignment. However, the nature of genetic algorithms makes the complexity extremely high due to the redundancy provided by the different operators. The aim of this paper is to study some proposed GA solutions provided for MSA and to compare them using some criteria which we believe any solution should comply with in matters of representativeness, closeness and original sequence invariance.

Article Details

Survey of the use of genetic algorithm for multiple sequence alignment

Authors: Mohamed Tahar Ben Othman

Publisher: Science Publishing Corporation

Journal: Journal of Advanced Computer Science & Technology Vol.5 Issue 2, pp.28-33

Document Type: Article

Publication Date: 8/2016

Document Language: English

Document ID (Org No.): 2001388483

pISSN: 2227-4332

Genetic Algorithms

Issues

Authors:

Mohamed Tahar Ben Othman

Publisher:

Science Publishing Corporation

Journal:

Journal of Advanced Computer Science & Technology Vol.5 Issue 2, pp.28-33

Document Type:

Article

Publication Date:

8/2016

Document Language:

English

Document ID (Org No.):

2001388483

pISSN:

2227-4332