Literature DB >> 11389459

Evolutionary computation.

J A Foster1.   

Abstract

Evolution does not require DNA, or even living organisms. In computer science, the field known as 'evolutionary computation' uses evolution as an algorithmic tool, implementing random variation, reproduction and selection by altering and moving data within a computer. This harnesses the power of evolution as an alternative to the more traditional ways to design software or hardware. Research into evolutionary computation should be of interest to geneticists, as evolved programs often reveal properties - such as robustness and non-expressed DNA - that are analogous to many biological phenomena.

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Year:  2001        PMID: 11389459     DOI: 10.1038/35076523

Source DB:  PubMed          Journal:  Nat Rev Genet        ISSN: 1471-0056            Impact factor:   53.242


  14 in total

1.  Design of genetic networks with specified functions by evolution in silico.

Authors:  Paul François; Vincent Hakim
Journal:  Proc Natl Acad Sci U S A       Date:  2004-01-02       Impact factor: 11.205

2.  The metapopulation genetic algorithm: An efficient solution for the problem of large phylogeny estimation.

Authors:  Alan R Lemmon; Michel C Milinkovitch
Journal:  Proc Natl Acad Sci U S A       Date:  2002-07-25       Impact factor: 11.205

Review 3.  Designing antimicrobial peptides: form follows function.

Authors:  Christopher D Fjell; Jan A Hiss; Robert E W Hancock; Gisbert Schneider
Journal:  Nat Rev Drug Discov       Date:  2011-12-16       Impact factor: 84.694

4.  In silico evolution of functional morphology: A test on bone tissue biomechanics.

Authors:  Emmanuel de Margerie; Paul Tafforeau; Lalaonirina Rakotomanana
Journal:  J R Soc Interface       Date:  2006-10-22       Impact factor: 4.118

5.  Automated reverse engineering of nonlinear dynamical systems.

Authors:  Josh Bongard; Hod Lipson
Journal:  Proc Natl Acad Sci U S A       Date:  2007-06-06       Impact factor: 11.205

6.  Selection methods regulate evolution of cooperation in digital evolution.

Authors:  Pawel Lichocki; Dario Floreano; Laurent Keller
Journal:  J R Soc Interface       Date:  2013-10-23       Impact factor: 4.118

7.  A Model for Designing Adaptive Laboratory Evolution Experiments.

Authors:  Ryan A LaCroix; Bernhard O Palsson; Adam M Feist
Journal:  Appl Environ Microbiol       Date:  2017-03-31       Impact factor: 4.792

8.  Microbial Communities as Experimental Units.

Authors:  Mitch D Day; Daniel Beck; James A Foster
Journal:  Bioscience       Date:  2011-05       Impact factor: 8.589

9.  Defining and simulating open-ended novelty: requirements, guidelines, and challenges.

Authors:  Wolfgang Banzhaf; Bert Baumgaertner; Guillaume Beslon; René Doursat; James A Foster; Barry McMullin; Vinicius Veloso de Melo; Thomas Miconi; Lee Spector; Susan Stepney; Roger White
Journal:  Theory Biosci       Date:  2016-05-19       Impact factor: 1.919

10.  A dynamic model for stem cell homeostasis and patterning in Arabidopsis meristems.

Authors:  Tim Hohm; Eckart Zitzler; Rüdiger Simon
Journal:  PLoS One       Date:  2010-02-12       Impact factor: 3.240

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