Restarting and recentering genetic algorithm variations for DNA fragment assembly: The necessity of a multi-strategy approach

DNA Fragment assembly – an NP-Hard problem – is one of the major steps in of DNA sequencing. Multiple strategies have been used for this problem, including greedy graph-based algorithms, deBruijn graphs, and the overlap-layout-consensus approach. This study focuses on the overlap-layout-consensus ap...

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Vydané v:BioSystems Ročník 150; s. 35 - 45
Hlavní autori: Hughes, James Alexander, Houghten, Sheridan, Ashlock, Daniel
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Ireland Elsevier B.V 01.12.2016
Predmet:
Algorithms
Animals
Artificial Intelligence
DNA - genetics
DNA fragment assembly problem
DNA Fragmentation
Evolutionary algorithm
Humans
Island Model
Recentering-restarting
Representation
Ring Species
Sequence Analysis, DNA - methods
Representation
DNA fragment assembly problem
Island Model
Evolutionary algorithm
Recentering-restarting
Ring Species
ISSN:0303-2647, 1872-8324
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Shrnutí:DNA Fragment assembly – an NP-Hard problem – is one of the major steps in of DNA sequencing. Multiple strategies have been used for this problem, including greedy graph-based algorithms, deBruijn graphs, and the overlap-layout-consensus approach. This study focuses on the overlap-layout-consensus approach. Heuristics and computational intelligence methods are combined to exploit their respective benefits. These algorithm combinations were able to produce high quality results surpassing the best results obtained by a number of competitive algorithms specially designed and tuned for this problem on thirteen of sixteen popular benchmarks. This work also reinforces the necessity of using multiple search strategies as it is clearly observed that algorithm performance is dependent on problem instance; without a deeper look into many searches, top solutions could be missed entirely.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:0303-2647
1872-8324
DOI:10.1016/j.biosystems.2016.08.001