Among the sequence selection and comparison problems, the Far From Most String Problem (FFMSP) is one of the computationally hardest with applications in several fields, including molecular biology where one is interested in creating diagnostic probes for bacterial infections or in discovering potential drug targets. In this article, several hybrid metaheuristics are described and tested. Extensive comparative experiments on a large set of randomly generated test instances indicate that these randomized hybrid techniques are both effective and efficient. © 2013 Springer-Verlag.

Hybrid metaheuristics for the far from most string problem

Ferone D.;
2013

Abstract

Among the sequence selection and comparison problems, the Far From Most String Problem (FFMSP) is one of the computationally hardest with applications in several fields, including molecular biology where one is interested in creating diagnostic probes for bacterial infections or in discovering potential drug targets. In this article, several hybrid metaheuristics are described and tested. Extensive comparative experiments on a large set of randomly generated test instances indicate that these randomized hybrid techniques are both effective and efficient. © 2013 Springer-Verlag.
978-3-642-38515-5
978-3-642-38516-2
Combinatorial optimization
Computational biology
Hybrid metaheuristics
Molecular structure prediction
Protein and sequences alignment
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/20.500.11770/307519
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