A Parallel Niched Pareto Evolutionary Algorithm for Multiple Sequence Alignment
Multiple sequence alignment is one of the most common tasks in Bioinformatics. However, there are not biologically accurate methods for performing sequence alignment. Genetic Algorithms are adaptive search methods which perform well in large and complex spaces, such as the ones present when aligning a set of sequences. Parallel Genetic Algorithms, not only increase the speed up of the search, but also improve its efficiency, presenting results that are better than those provided by the sum of several sequential Genetic Algorithms. Although these methods are often used to optimize a single objective, they can also be used in multidimensional domains, finding all possible tradeoffs among multiple conflicting objectives. Parallel AlineaGA is an evolutionary algorithm which makes use of a Parallel Genetic Algorithm for performing multiple sequence alignment. We present a multiple objective approach of Parallel AlineaGA that uses a Parallel Niched Pareto Genetic Algorithm. We compare the performance of both versions using eight BAliBASE datasets. We also measure up the quality of the obtained solutions with the ones achieved by T-Coffee and ClustalW2, allowing us to observe that our algorithm reaches for better solutions in the majority of the datasets.
KeywordsGenetic Algorithm Multiple Sequence Alignment Pareto Front Message Passing Interface Parallel Genetic Algorithm
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