Efficient Bio-molecules Sequencing Using Multi-Objective Optimization and High-Performance Computing

Reformist approaches to multiple sequence alignment (MSA) needed dropping an MSA at each alignment phase. But it illustrates the evidence of gap scoring rates for exact alignment. In the literature it has three types of sequences; Ribonucleic Acid, Deoxyribonucleic Acid, and Proteins. The MSA has been represented as a sequence alignment problem and solved using dynamic programming techniques. Due to the presence of a huge length of sequences in the MSA, the alignment of these sequences is complicated and time taking, even though sometimes optimal solution is not obtained. Therefore, MSA in the literature is categorized into the NP-complete. The genetic algorithm (GA) and its variations have been successfully used for solving NP-complete and thus prominently may be used for MSA problem that maximizes the sequence similarity. To address an MSA problem, an effective GA-based algorithm is explored. The "Non-dominated Sorting Genetic Algorithm (GA)-II" (NSGA-II) is extensively used model to explore this problem. To solve the MSA optimization problems, an adapted NSGA-II has been proposed and compressive analysis has been performed to verify the potency of the work deliberated here.