Document Type

Article

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

Disciplines

Bioinformatics | Biology | Biotechnology | Life Sciences

CIT Disciplines

1.6 BIOLOGICAL SCIENCES

Publication Details

Bioengineered

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.

Manning_2013_et_al_T1-10.4161_bioe.23041.csv (1 kB)
Table 1. Performance of HGA-PSO, backtracking-EA, aging-AIS and ClonalgI on a set of 7 proteins

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