Multiple Sequence Alignment by Differential Evolutionary Algorithm with New Mutant

Abstract

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.