Multiple Sequence Alignment by Differential Evolutionary Algorithm with New Mutant
Lakshmi Naga Jayaprada. Gavarraju1, K. Karteeka Pavan2
1Lakshmi Naga Jayaprada. Gavarraju, Assoc.Prof, Dept. of Computer Science & Engineering, Narasaraopeta Engineering College [Autonomous], Narasaraopet, Guntur(Dt), A.P., India.
2Kanadam Karteeka Pavan, Professor & Head Department of Computer Applications, R.V.R.& J.C.College of Engineering [Autonomous], Chowdavaram , Guntur , A.P., India.
Manuscript received on November 19, 2019. | Revised Manuscript received on November 29 2019. | Manuscript published on 30 November, 2019. | PP: 9892-9897 | Volume-8 Issue-4, November 2019. | Retrieval Number: D9208118419/2019©BEIESP | DOI: 10.35940/ijrte.D9208.118419
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
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.
Keywords: MSA, GA, DE, TLBO.
Scope of the Article: Parallel and Distributed Algorithms.