A fast quantum mechanical algorithm for database search
STOC '96 Proceedings of the twenty-eighth annual ACM symposium on Theory of computing
Explorations in quantum computing
Explorations in quantum computing
Multiple sequence alignment using tabu search
APBC '04 Proceedings of the second conference on Asia-Pacific bioinformatics - Volume 29
Algorithms for quantum computation: discrete logarithms and factoring
SFCS '94 Proceedings of the 35th Annual Symposium on Foundations of Computer Science
A quantum evolutionary algorithm for effective multiple sequence alignment
EPIA'05 Proceedings of the 12th Portuguese conference on Progress in Artificial Intelligence
IEEE Transactions on Evolutionary Computation
Multiple Sequence Alignment with Evolutionary-Progressive Method
ICANNGA '07 Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part I
A New Quantum Evolutionary Local Search Algorithm for MAX 3-SAT Problem
HAIS '08 Proceedings of the 3rd international workshop on Hybrid Artificial Intelligence Systems
RBT-L: A location based approach for solving the Multiple Sequence Alignment problem
International Journal of Bioinformatics Research and Applications
Quantum-inspired evolutionary algorithms: a survey and empirical study
Journal of Heuristics
A new greedy randomised adaptive search procedure for multiple sequence alignment
International Journal of Bioinformatics Research and Applications
Novel hybrid genetic algorithm for progressive multiple sequence alignment
International Journal of Bioinformatics Research and Applications
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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.