Parametric optimization of sequence alignment
SODA '92 Proceedings of the third annual ACM-SIAM symposium on Discrete algorithms
Algorithms on strings, trees, and sequences: computer science and computational biology
Algorithms on strings, trees, and sequences: computer science and computational biology
A multiple objective evolutionary algorithm for multiple sequence alignment
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Methodology to select solutions from the pareto-optimal set: a comparative study
Proceedings of the 9th annual conference on Genetic and evolutionary computation
An overview of evolutionary algorithms in multiobjective optimization
Evolutionary Computation
An efficient non-dominated sorting method for evolutionary algorithms
Evolutionary Computation
Bioinformatics
Multi-objective pairwise RNA sequence alignment
Bioinformatics
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Reducing the run-time complexity of multiobjective EAs: The NSGA-II and other algorithms
IEEE Transactions on Evolutionary Computation
A Multiobjective Optimization-Based Evolutionary Algorithm for Constrained Optimization
IEEE Transactions on Evolutionary Computation
A Simulated Annealing-Based Multiobjective Optimization Algorithm: AMOSA
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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Sequence alignment using evolutionary profiles is a commonly employed tool when investigating a protein. Many profile-profile scoring functions have been developed for use in such alignments, but there has not yet been a comprehensive study of Pareto optimal pairwise alignments for combining multiple such functions. We show that the problem of generating Pareto optimal pairwise alignments has an optimal substructure property, and develop an efficient algorithm for generating Pareto optimal frontiers of pairwise alignments. All possible sets of two, three, and four profile scoring functions are used from a pool of 11 functions and applied to 588 pairs of proteins in the ce_ref data set. The performance of the best objective combinations on ce_ref is also evaluated on an independent set of 913 protein pairs extracted from the BAliBASE RV11 data set. Our dynamic-programming-based heuristic approach produces approximated Pareto optimal frontiers of pairwise alignments that contain comparable alignments to those on the exact frontier, but on average in less than 1/58th the time in the case of four objectives. Our results show that the Pareto frontiers contain alignments whose quality is better than the alignments obtained by single objectives. However, the task of identifying a single high-quality alignment among those in the Pareto frontier remains challenging.