TY - GEN
T1 - A niched pareto genetic algorithm
T2 - 2nd International Conference on Agents and Artificial Intelligence, ICAART 2010
AU - Da Silva, Fernando José Mateus
AU - Pérez, Juan Manuel Sánchez
AU - Pulido, Juan Antonio Gómez
AU - Rodriguez, Miguel A.Vega
PY - 2010
Y1 - 2010
N2 - 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.
AB - 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.
KW - Bioinformatics
KW - Equivalence class sharing
KW - Genetic algorithms
KW - Multiobjective optimization
KW - Multiple sequence alignments
KW - Niched pareto
UR - http://www.scopus.com/inward/record.url?scp=77956294372&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:77956294372
SN - 9789896740221
SN - 9789896740214
T3 - ICAART 2010 - 2nd International Conference on Agents and Artificial Intelligence, Proceedings
SP - 323
EP - 329
BT - ICAART 2010 - 2nd International Conference on Agents and Artificial Intelligence, Proceedings
Y2 - 22 January 2010 through 24 January 2010
ER -