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Items: 1 to 20 of 107

1.

A solution to the challenge of optimization on ''golf-course''-like fitness landscapes.

Melo HP, Franks A, Moreira AA, Diermeier D, Andrade JS Jr, Amaral LA.

PLoS One. 2013 Nov 5;8(11):e78401. doi: 10.1371/journal.pone.0078401. eCollection 2013.

2.

Artificial evolution by viability rather than competition.

Maesani A, Fernando PR, Floreano D.

PLoS One. 2014 Jan 29;9(1):e86831. doi: 10.1371/journal.pone.0086831. eCollection 2014.

3.

Coevolutionary computation.

Paredis J.

Artif Life. 1995 Summer;2(4):355-75.

PMID:
8942053
4.

Predicting the evolution of sex on complex fitness landscapes.

Misevic D, Kouyos RD, Bonhoeffer S.

PLoS Comput Biol. 2009 Sep;5(9):e1000510. doi: 10.1371/journal.pcbi.1000510. Epub 2009 Sep 18.

5.

Toward a theory of evolutionary computation.

Eberbach E.

Biosystems. 2005 Oct;82(1):1-19.

PMID:
16102892
6.

Efficient and scalable Pareto optimization by evolutionary local selection algorithms.

Menczer F, Degeratu M, Street WN.

Evol Comput. 2000 Summer;8(2):223-47.

PMID:
10843522
7.

Fitness landscapes, memetic algorithms, and greedy operators for graph bipartitioning.

Merz P, Freisleben B.

Evol Comput. 2000 Spring;8(1):61-91.

PMID:
10753231
8.

On the Performance of Different Genetic Programming Approaches for the SORTING Problem.

Wagner M, Neumann F, Urli T.

Evol Comput. 2015 Winter;23(4):583-609. doi: 10.1162/EVCO_a_00149. Epub 2015 Apr 14.

PMID:
25870929
9.

GENOMEPOP: a program to simulate genomes in populations.

Carvajal-Rodríguez A.

BMC Bioinformatics. 2008 Apr 30;9:223. doi: 10.1186/1471-2105-9-223.

10.

Naturally selecting solutions: the use of genetic algorithms in bioinformatics.

Manning T, Sleator RD, Walsh P.

Bioengineered. 2013 Sep-Oct;4(5):266-78. doi: 10.4161/bioe.23041. Epub 2012 Dec 6. Review.

11.

Improved evolutionary optimization from genetically adaptive multimethod search.

Vrugt JA, Robinson BA.

Proc Natl Acad Sci U S A. 2007 Jan 16;104(3):708-11. Epub 2007 Jan 10.

12.

Optimization by hierarchical mutant production.

Schober A, Thuerk M, Eigen M.

Biol Cybern. 1993;69(5-6):493-501.

PMID:
8274548
13.

Varying environments can speed up evolution.

Kashtan N, Noor E, Alon U.

Proc Natl Acad Sci U S A. 2007 Aug 21;104(34):13711-6. Epub 2007 Aug 14.

14.

Genetic algorithms for finite mixture model based voxel classification in neuroimaging.

Tohka J, Krestyannikov E, Dinov ID, Graham AM, Shattuck DW, Ruotsalainen U, Toga AW.

IEEE Trans Med Imaging. 2007 May;26(5):696-711.

15.

Evolutionary optimization of a hierarchical object recognition model.

Schneider G, Wersing H, Sendhoff B, Körner E.

IEEE Trans Syst Man Cybern B Cybern. 2005 Jun;35(3):426-37.

PMID:
15971912
16.

Multimodal optimization using a bi-objective evolutionary algorithm.

Deb K, Saha A.

Evol Comput. 2012 Spring;20(1):27-62. doi: 10.1162/EVCO_a_00042. Epub 2011 Dec 2.

PMID:
21591888
17.

Sensitivity versus accuracy in multiclass problems using memetic Pareto evolutionary neural networks.

Fernández Caballero JC, Martínez FJ, Hervás C, Gutiérrez PA.

IEEE Trans Neural Netw. 2010 May;21(5):750-70. doi: 10.1109/TNN.2010.2041468. Epub 2010 Mar 11.

PMID:
20227976
18.

Quantifying uncertainty in NMR T2 spectra using Monte Carlo inversion.

Prange M, Song YQ.

J Magn Reson. 2009 Jan;196(1):54-60. doi: 10.1016/j.jmr.2008.10.008. Epub 2008 Oct 12.

PMID:
18952474
19.

Dynamics and evolution of stochastic bistable gene networks with sensing in fluctuating environments.

Ribeiro AS.

Phys Rev E Stat Nonlin Soft Matter Phys. 2008 Dec;78(6 Pt 1):061902. Epub 2008 Dec 2.

PMID:
19256863
20.

Genetic diversity as an objective in multi-objective evolutionary algorithms.

Toffolo A, Benini E.

Evol Comput. 2003 Summer;11(2):151-67.

PMID:
12875667

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