A bi-objective function optimization approach for multiple sequence alignment using genetic algorithm

Abstract

Multiple sequence alignment (MSA) is characterized as a very high computational complex problem. Therefore, MSA problem cannot be solved by exhaustive methods. Nowadays, MSA is being solved by optimizing more than one objective simultaneously. In this paper, we propose a new genetic algorithm based alignment technique, named bi-objective sequence alignment using genetic algorithm (BSAGA). The novelty of this approach is its selection process. One part of the population is selected based on the Sum of Pair, and rest is selected based on Total Conserve Columns. We applied integer-based chromosomal coding to represent only the gap positions in an alignment. Such representation improves the search technique to reach an optimum even for longer sequences. We tested and compared the alignment score of BSAGA with other relevant alignment techniques on BAliBASE and SABmark. The BSAGA shows better performance than others do, which was further proved by the Wilcoxon sign test.