A novel multi-objective genetic algorithm for multiple sequence alignment

There is no well-accepted theoretical model for multiple sequence alignment. An algorithm is accepted as a good method for multiple sequence alignment if it produces better fitness scores with respect to the benchmark datasets. For this purpose, we propose an efficient method using multi-objective genetic algorithm to discover optimal alignments in multiple sequence data. The main advantage of our approach is that a large number of trade-off alignments can be obtained by a single run with respect to conflicting objectives: alignment length minimisation and similarity and support maximisation. We compare our method with the four well-known multiple sequence alignment methods, MUSCLE, ClustalW, SAGA and MSA-GA. The first two of them are progressive methods, and the other two are based on evolutionary algorithms. Experimental results on the BAliBASE 2.0 database demonstrate that our method found better solutions than the others for most of the cases in terms of accuracy.