Optimizing Multiple Sequence Alignment using Multi-Objective Genetic Algorithms

Abstract

The multiple sequence alignment (MSA) issues are contingent on dropping an MSA to a rectilinear sketch for every alignment phase. Though, these indicate the damage of information desired for precise alignment and gap scoring rate evidence. The single-objective and multi-objective techniques can be applied to the MSA problem. MSA can be classified into the NP-complete class of problems. Due to this classification, the genetic algorithm (GA) and variants that effectively solved the NP-complete class of problems can also solve the MSA problem to maximize the similarities among sequences. In this work, the dynamic programming-based algorithm for solving the MSA problems in bioinformatics has been discussed. A novel approach based on GA and variants is suggested for solving an MSA problem. MSA problem can be visualized as multi-objective optimization, so the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) can be applied. The single-objective and the multi-objective optimization problem are mathematically formulated and constraints related to both the objectives are identified. An adapted GA and NSGA-II are suggested to the MSA optimization problems.