Naturally selecting solutions The use of genetic algorithms in bioinformatics

For decades, computer scientists have looked to nature for biologically inspired solutions to computational problems; ranging from robotic control to scheduling optimization. Paradoxically, as we move deeper into the post-genomics era, the reverse is occurring, as biologists and bioinformaticians lo...

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Veröffentlicht in:	Bioengineered Jg. 4; H. 5; S. 266 - 278
Hauptverfasser:	Manning, Timmy, Sleator, Roy D, Walsh, Paul
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States Taylor & Francis 01.09.2013 Landes Bioscience
Schlagworte:	Algorithms Computational Biology - methods Computer Simulation Crossing Over, Genetic genetic algorithm Genetic Fitness Genotype Models, Genetic multiple sequence alignment optimization Phenotype protein structure prediction Selection, Genetic Sequence Alignment genetic algorithm optimization protein structure prediction multiple sequence alignment
ISSN:	2165-5979, 2165-5987, 2165-5987
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Abstract	For decades, computer scientists have looked to nature for biologically inspired solutions to computational problems; ranging from robotic control to scheduling optimization. Paradoxically, as we move deeper into the post-genomics era, the reverse is occurring, as biologists and bioinformaticians look to computational techniques, to solve a variety of biological problems. One of the most common biologically inspired techniques are genetic algorithms (GAs), which take the Darwinian concept of natural selection as the driving force behind systems for solving real world problems, including those in the bioinformatics domain. Herein, we provide an overview of genetic algorithms and survey some of the most recent applications of this approach to bioinformatics based problems.
AbstractList	For decades, computer scientists have looked to nature for biologically inspired solutions to computational problems; ranging from robotic control to scheduling optimization. Paradoxically, as we move deeper into the post-genomics era, the reverse is occurring, as biologists and bioinformaticians look to computational techniques, to solve a variety of biological problems. One of the most common biologically inspired techniques are genetic algorithms (GAs), which take the Darwinian concept of natural selection as the driving force behind systems for solving real world problems, including those in the bioinformatics domain. Herein, we provide an overview of genetic algorithms and survey some of the most recent applications of this approach to bioinformatics based problems.For decades, computer scientists have looked to nature for biologically inspired solutions to computational problems; ranging from robotic control to scheduling optimization. Paradoxically, as we move deeper into the post-genomics era, the reverse is occurring, as biologists and bioinformaticians look to computational techniques, to solve a variety of biological problems. One of the most common biologically inspired techniques are genetic algorithms (GAs), which take the Darwinian concept of natural selection as the driving force behind systems for solving real world problems, including those in the bioinformatics domain. Herein, we provide an overview of genetic algorithms and survey some of the most recent applications of this approach to bioinformatics based problems. For decades, computer scientists have looked to nature for biologically inspired solutions to computational problems; ranging from robotic control to scheduling optimization. Paradoxically, as we move deeper into the post-genomics era, the reverse is occurring, as biologists and bioinformaticians look to computational techniques, to solve a variety of biological problems. One of the most common biologically inspired techniques are genetic algorithms (GAs), which take the Darwinian concept of natural selection as the driving force behind systems for solving real world problems, including those in the bioinformatics domain. Herein, we provide an overview of genetic algorithms and survey some of the most recent applications of this approach to bioinformatics based problems.
Author	Manning, Timmy Sleator, Roy D Walsh, Paul
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SubjectTerms	Algorithms Computational Biology - methods Computer Simulation Crossing Over, Genetic genetic algorithm Genetic Fitness Genotype Models, Genetic multiple sequence alignment optimization Phenotype protein structure prediction Selection, Genetic Sequence Alignment
Subtitle	The use of genetic algorithms in bioinformatics
Title	Naturally selecting solutions
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