Application of genetic algorithm by influencing the crossover parameters for multiple sequence alignment

In Bioinformatics, the more standard approach to identify the protein functionalities, protein structures, pattern identification and evolutionary relationships between the organisms are possible using the sequence alignment. The similarity of sequences is found between the organisms by using RNA/DNA/Nucleotide sequences. Multiple Sequence Alignment is denoted as MSA is a method of arranging three, or further whole sequences simultaneously to find the similarity between them which leads to identify biological factors such as Drug design, the function of the protein, etc. In this paper, an upgraded Genetic Algorithm has been projected to discover an optimum solution for MSA by influencing different crossover parameters. For generating and preserving the variety of candidate solutions, the various classes of crossover operators are responsible. Also, the multi-objective optimization (MOO) technique was employed by maximizing the sequence similarity and minimizing the gap penalty to obtain the Pareto front optimal solution. The benchmark database named BAliBASE 3.0 was employed to evaluate the achievement of different crossover operators which influences the quality of sequence alignment. The final outcome revealed the significant enhancement in the sequence alignment with the influence of crossover operators. The results have been compared with online efficient tools such as T-Coffee, Clustal Q, MAFFT and Kalign and other optimization algorithms such as Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Genetic Algorithm (GA) and Artificial Bee Colony (ABC) algorithm and found that the projected method achieves improved results.