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.
Disciplines
Bioinformatics | Biology | Biotechnology | Life Sciences
DOI
10.4161/bioe.23041
Full Publication Date
October 2012
Publisher
Taylor & Francis
Funder Name 1
European Commission
Award Number 1
FP7-PEOPLE2012-IAPP
Resource Type
review
Access Rights
open access
Open Access Route
Undetermined
License Condition
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Alternative Identifier
https://www.tandfonline.com/doi/full/10.4161/bioe.23042
Recommended Citation
Bioengineered. (2013). Naturally selecting solutions. Available at: https://www.tandfonline.com/doi/full/10.4161/bioe.23041
Table 1. Performance of HGA-PSO, backtracking-EA, aging-AIS and ClonalgI on a set of 7 proteins
Included in
Bioinformatics Commons, Biology Commons, Biotechnology Commons
Publication Details
Bioengineered
Originally published by Landes Sciences, which was acquired by Taylor & Francis in June 2014