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A niched pareto genetic algorithm: For multiple sequence alignment optimization
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| The alignment of molecular sequences is a recurring task in bioinformatics, but it is not a trivial problem. The size and complexity of the search space involved difficult the task of finding the optimal alignment of a set of sequences. Due to its adaptive capacity in large and complex spaces, Genetic Algorithms emerge as good candidates for this problem. Although they are often used in single objective domains, its use in multidimensional problems allows finding a set of solutions which provide the best possible optimization of the objectives - the Pareto front. Niching methods, such as sharing, distribute these solutions in space, maximizing their diversity along the front. We present a niched Pareto Genetic Algorithm for sequence alignment which we have tested with six BAliBASE alignments, taking conclusions regarding population evolution and quality of the final results. Whereas methods for finding the best alignment are mathematical, not biological, having a set of solutions which facilitate experts' choice, is a possibility to consider. | 1.53 MB | Adobe PDF |
Orientador(es)
Resumo(s)
The alignment of molecular sequences is a recurring task in bioinformatics, but it is not a trivial problem. The size and complexity of the search space involved difficult the task of finding the optimal alignment of a set of sequences. Due to its adaptive capacity in large and complex spaces, Genetic Algorithms emerge as good candidates for this problem. Although they are often used in single objective domains, its use in multidimensional problems allows finding a set of solutions which provide the best possible optimization of the objectives - the Pareto front. Niching methods, such as sharing, distribute these solutions in space, maximizing their diversity along the front. We present a niched Pareto Genetic Algorithm for sequence alignment which we have tested with six BAliBASE alignments, taking conclusions regarding population evolution and quality of the final results. Whereas methods for finding the best alignment are mathematical, not biological, having a set of solutions which facilitate experts' choice, is a possibility to consider.
Descrição
Conference name - 2nd International Conference on Agents and Artificial Intelligence, ICAART 2010; Conference date - 22 January 2010 - 24 January 2010; Conference code - 81591
Fontes: https://scholar.google.com/scholar?q=A%20niched%20pareto%20genetic%20algorithm%3A%20For%20multiple%20sequence%20alignment%20optimization https://www.researchgate.net/publication/221539748_A_Niched_Pareto_Genetic_Algorithm_-_For_Multiple_Sequence_Alignment_Optimization
Fontes: https://scholar.google.com/scholar?q=A%20niched%20pareto%20genetic%20algorithm%3A%20For%20multiple%20sequence%20alignment%20optimization https://www.researchgate.net/publication/221539748_A_Niched_Pareto_Genetic_Algorithm_-_For_Multiple_Sequence_Alignment_Optimization
Palavras-chave
Multiple sequence alignments Genetic algorithms Multiobjective optimization Niched Pareto Equivalence class sharing Bioinformatics
Contexto Educativo
Citação
Silva, Fernando & Sánchez-Pérez, Juan & Gómez-Pulido, Juan A. & Vega-Rodríguez, Miguel A.. (2010). A Niched Pareto Genetic Algorithm - For Multiple Sequence Alignment Optimization. ICAART 2010 - 2nd International Conference on Agents and Artificial Intelligence, Proceedings. 1. 323-329. DOI: https://doi.org/10.5220/0002729303230329.
Editora
SciTePress
Licença CC
Sem licença CC