Genetic approach to biosequence alignment (GABA)

Alignment of biological sequences is one of the most important and challenging tasks in computational biology and bioinformatics and is inherently complex. The algorithmic solutions are characterized by huge computational and memory requirements. We present an approach to biosequences alignment, that uses a genetic algorithm to search the solution space. By converting a given biomolecular sequence into an optimal or near optimal search problem in the solution space, the genetic algorithm efficiently and robustly finds the solution for the multiple sequence alignment. Our approach is different from previous similar approaches [1, 2] in the sense that it allows pairwise alignment in each generation and does not utilize dynamic programming at any stage. This paper presents our approach and demonstrates its performances with experiments on real DNA datasets.