On solving the most strings with few bad columns problem: An ILP model and heuristics

Lizárraga, Evelia; Blesa, Miguel A.; Blum, Christian; Raidl, Günther R.

doi:10.1109/inista.2015.7276795

Cited by 3 publications

(7 citation statements)

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“…In this work we, first, introduce an alternative way of making use of the greedy strategy that was introduced in [5]. This new way of using the greedy strategy is shown to obtain results comparable to the ones obtained by the greedy-based pilot method in significantly less computation time.…”

Section: Our Contributionmentioning

confidence: 98%

“…Moreover, they state that the problem is a generalization of the problem of finding tandem repeats in a string [4]. In a preliminary version of this article (see [5]) we introduced the first integer linear programming (ILP) model for the MSFBC problem. Moreover, we devised a greedy strategy which was further applied in the context of a so-called pilot method [14].…”

Section: Existing Workmentioning

confidence: 99%

“…In [5] we introduced the following greedy strategy for completing a solution when given a non-empty partial solution-that is, a subset of the input strings S p ⊆ I-as input. Henceforth, bc(S p ) denotes the number of bad columns with respect to S p , that is, the number of columns j such that at least two strings…”

Section: Heuristic Approachesmentioning

confidence: 99%

“…In [5] we introduced two different ways of using procedure MakeSolutionComplete(S p ) when starting with an empty solution. In the first one, exactly one string from I is chosen based on a criterion related to letter frequencies.…”

Section: 1mentioning

confidence: 99%

“…Apart from Greedy-F, in [5] we made use of procedure MakeSolutionComplete(S p ) in the context of a pilot method [14]. Procedure MakeSolutionComplete(S p ) was used at each level of the search tree in order to evaluate each possible partial solution.…”

Section: Multi-start Greedy Approachmentioning

confidence: 99%

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Large neighborhood search for the most strings with few bad columns problem

et al. 2016

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In this work we consider the following N P -hard combinatorial optimization problem from computational biology. Given a set of input strings of equal length, the goal is to identify a maximum cardinality subset of strings that dier maximally in a pre-dened number of positions. First of all we introduce an integer linear programming model for this problem. Second, two variants of a rather simple greedy strategy are proposed. Finally, a large neighborhood search algorithm is presented. A comprehensive experimental comparison among the proposed techniques shows, rst, that larger neighborhood search generally outperforms both greedy strategies. Second, while large neighborhood search shows to be competitive with the stand-alone application of CPLEX for small and medium sized problem instances, it outperforms CPLEX in the context of larger instances.Keywords: Most strings with few bad columns, integer linear programming, large neighborhood search. * A preliminary version of this work appeared at

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Section: Our Contributionmentioning

confidence: 98%

Section: Existing Workmentioning

confidence: 99%

Section: Heuristic Approachesmentioning

confidence: 99%

Section: 1mentioning

confidence: 99%

Section: Multi-start Greedy Approachmentioning

confidence: 99%

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Large neighborhood search for the most strings with few bad columns problem

et al. 2016

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2016

Metaheuristics for String Problems in Bio‐Informatics

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On the role of metaheuristic optimization in bioinformatics

Calvet

Benito

Juan

et al. 2022

Int Trans Operational Res

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Metaheuristic algorithms are employed to solve complex and large-scale optimization problems in many different fields, from transportation and smart cities to finance. This paper discusses how metaheuristic algorithms are being applied to solve different optimization problems in the area of bioinformatics. While the text provides references to many optimization problems in the area, it focuses on those that have attracted more interest from the optimization community. Among the problems analyzed, the paper discusses in more detail the molecular docking problem, the protein structure prediction, phylogenetic inference, and different string problems. In addition, references to other relevant optimization problems are also given, including those related to medical imaging or gene selection for classification. From the previous analysis, the paper generates insights on research opportunities for the Operations Research and Computer Science communities in the field of bioinformatics.

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On solving the most strings with few bad columns problem: An ILP model and heuristics

Cited by 3 publications

References 15 publications

Large neighborhood search for the most strings with few bad columns problem

Large neighborhood search for the most strings with few bad columns problem

Bibliography

On the role of metaheuristic optimization in bioinformatics

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