An evolutionary approach for performing multiple sequence alignment

Despite of being a very common task in bioinformatics, multiple sequence alignment is not a trivial matter. Arranging a set of molecular sequences to reveal their similarities and their differences is often hardened by the complexity and the size of the search space involved, which undermine the approaches that try to explore exhaustively the solution's search space. Due to its nature, Genetic Algorithms, which are prone for general combinatorial problems optimization in large and complex search spaces, emerge as serious candidates to tackle with the multiple sequence alignment problem. We have developed an evolutionary approach, AlineaGA, which uses a Genetic Algorithm with local search optimization embedded on its mutation operators for performing multiple sequence alignment. Now, we have enhanced its selection method by employing an elitist strategy, and we have also developed a new crossover operator. These transformations allow AlineaGA to improve its robustness and to obtain better fit solutions. Also, we have studied the effect of the mutation probability in solutions' evolution by analyzing the performance of the whole population throughout generations. We conclude that increasing the mutation probability leads to better solutions in fewer generations and that the mutation operators have a dramatic effect in this particular domain.