scholarly journals Optimization of multiple sequence alignment methodologies using a multiobjective evolutionary algorithm based on NSGA-II

2012 ◽  
Author(s):  
Francisco Ortuno ◽  
Javier P. Florido ◽  
Jose M. Urquiza ◽  
Hector Pomares ◽  
Alberto Prieto ◽  
...  
Keyword(s):  
Evolutionary Algorithm ◽  
Sequence Alignment ◽  
Multiple Sequence Alignment ◽  
Nsga Ii ◽  
Multiple Sequence ◽  
Multiobjective Evolutionary Algorithm

scholarly journals Multiple Sequence Alignment by Differential Evolutionary Algorithm with New Mutant

2019 ◽  
Vol 8 (4) ◽  
pp. 9892-9897
Keyword(s):  
Evolutionary Algorithm ◽  
Sequence Alignment ◽  
Multiple Sequence Alignment ◽  
The Other ◽  
Bench Mark ◽  
Data Sets ◽  
Multiple Sequence ◽  
Differential Evolutionary Algorithm ◽  
Np Complete ◽  
Multiple Species

Multiple Sequence Alignment (MSA) is vital in Bioinformatics, helps in finding evolutionary relationships among multiple species. MSA is a NP-complete problem. Though there are a number of tools recent Meta-heuristics are found to be effective in solving MSA problem. Differential Evolutionary Algorithm (DE) is one of the optimization algorithms with various mutants. This work proposes a new mutant for DE, defined using local best and worst chromosomes with current generation population. The performance of the new mutant is evaluated using 50 well known bench mark data sets in sabre (SABMARK v1.65). The results are matched with all the other DE mutants, Genetic Algorithm (GA) and recent Teacher Learner Based Optimization algorithm (TLBO). The proposed DE mutant outperformed all the other DE mutants, GA and TLBO in solving MSA problem.

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