Traditionally Optimization of Multiple Genetic Sequence Alignment is based on statistics algorithms, which leads to loss of genetic characteristics and generate less feasible results. Sequence Alignment of genetic sequences need qualitative characteristics configuration at each level of MSA iteration, especially where characteristics inheritance based on specially genomes properties. Multiple Sequence Alignment optimization is one of the prominent areas in Bioinformatics where optimization problems occur due to mutation, uncertainty of large genetic data sets. Data Science and Mathematical Optimization Algorithm provides the combine solution for optimization problems related to genetic alignment. This research paper proposed a noval Hybrid Meta-Heuristic Optimization Genetic Alignment Algorithm (HMHOGA) to solve genetic alignment optimization problem at each phase of MSA iteration and also help to recognize occurrence of meta-heuristic characteristics. Proposed algorithm provides optimize alignment along with various evaluation factors like pair_sum(q), total_col_score(tc), standard_deviation_of_pair_matrix(z), reliability(r) and efficiency(e) based on CPU execution time. These evaluation factors act like as meta-heuristic knowledge base to optimize future alignments.
