A team of genetic algorithms for the multiple sequence alignment problem: preliminary results

2007 ◽  
Author(s):  
Carlos A. Brizuela ◽  
Elizabeth Luhrs-Olmos
Keyword(s):  
Genetic Algorithms ◽  
Sequence Alignment ◽  
Multiple Sequence Alignment ◽  
Multiple Sequence ◽  
Preliminary Results ◽  
Alignment Problem

Improving multiple sequence alignment biological accuracy through genetic algorithms

2013 ◽  
Vol 65 (3) ◽  
pp. 1076-1088 ◽  
Author(s):  
Miquel Orobitg ◽  
Fernando Cores ◽  
Fernando Guirado ◽  
Concepció Roig ◽  
Cedric Notredame
Keyword(s):  
Genetic Algorithms ◽  
Sequence Alignment ◽  
Multiple Sequence Alignment ◽  
Multiple Sequence

Resolving the multiple sequence alignment problem using biogeography-based optimization with multiple populations

2015 ◽  
Vol 13 (04) ◽  
pp. 1550016 ◽  
Author(s):  
El-Amine Zemali ◽  
Abdelmadjid Boukra
Keyword(s):  
Sequence Alignment ◽  
Dna Sequences ◽  
Multiple Sequence Alignment ◽  
Search Space ◽  
New Method ◽  
Average Score ◽  
Solution Quality ◽  
Multiple Sequence ◽  
Multiple Populations ◽  
Alignment Problem

The multiple sequence alignment (MSA) is one of the most challenging problems in bioinformatics, it involves discovering similarity between a set of protein or DNA sequences. This paper introduces a new method for the MSA problem called biogeography-based optimization with multiple populations (BBOMP). It is based on a recent metaheuristic inspired from the mathematics of biogeography named biogeography-based optimization (BBO). To improve the exploration ability of BBO, we have introduced a new concept allowing better exploration of the search space. It consists of manipulating multiple populations having each one its own parameters. These parameters are used to build up progressive alignments allowing more diversity. At each iteration, the best found solution is injected in each population. Moreover, to improve solution quality, six operators are defined. These operators are selected with a dynamic probability which changes according to the operators efficiency. In order to test proposed approach performance, we have considered a set of datasets from Balibase 2.0 and compared it with many recent algorithms such as GAPAM, MSA-GA, QEAMSA and RBT-GA. The results show that the proposed approach achieves better average score than the previously cited methods.

Sign in / Sign up

Export Citation Format

Share Document