Multiple sequence alignment by quantum genetic algorithm

In this paper we describe a new approach for the well known problem in bioinformatics: multiple sequence alignment (MSA). MSA is fundamental task as it represents an essential platform to conduct other tasks in bioinformatics such as the construction of phylogenetic trees, the structural and functional prediction of new protein sequences. Our approach merges between the classical genetic algorithm and some principles of the quantum computing like interference, measure, superposition, etc. It differs from other genetic methods of the literature by using a small population size and a less iteration required to find good quality alignments thanks to the used quantum principles: state superposition, interference, quantum mutation and quantum crossover. Another attractive feature of this method is its ability to provide an extensible platform for evaluating different objective functions. Experiments on a wide range of data sets have shown the effectiveness of the proposed approach and its ability to achieve good quality solutions comparing to those given by other popular multiple alignment programs.