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- W2296706549 abstract "In this paper, we implement Ant Colony Optimization (ACO) for sequence alignment. ACO is a meta-heuristic recently developed for nearest neighbor approximations in large, NP-hard search spaces. Here we use a genetic algorithm approach to evolve the best parameters for an ACO designed to align two sequences. We then used the best parameters found to interpolate approximate optimal parameters for a given string length within a range. The basis of our comparison is the alignment given by the Needleman-Wunsch algorithm. We found that ACO can indeed be applied to sequence alignment. While it is computationally expensive compared to other equivalent algorithms, it is a promising algorithm that can be readily applied to a variety of other biological problems." @default.
- W2296706549 created "2016-06-24" @default.
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- W2296706549 date "2014-06-04" @default.
- W2296706549 modified "2023-09-26" @default.
- W2296706549 title "ACO Implementation for Sequence Alignment with Genetic Algorithms." @default.
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