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Differential Evolution Entirely Parallel Method

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DEEP - Differential Evolution Entirely Parallel Method

This work is supported by RNF Grant 14-14-00302 and by “5-100-2020” Program of the Russian Ministry of Education and Science.

The previous funding included SYSPATHO FP7 project №260429, RFBR grants 10-01-00627-a, 11-01-00573-a, 11-04-001162-a and State Contract with Russian Ministry of Science №14.740.11.0166 and №11.519.11.6041

Description

Novel software framework to solve the inverse problem of mathematical modelling.

Presentation of the DEEP

kkozlov-presentation-deep.pdf

License

The code is licensed under GNU General Public License version 3.

How to cite in publications

   @article{Kozlov2016,
    title = {A Software for Parameter Optimization with {{Differential Evolution Entirely Parallel}} Method},
    author = {Kozlov, Konstantin and Samsonov, Alexander M. and Samsonova, Maria},
    year = {2016},
    month = aug,
    volume = {2},
    pages = {e74},
    issn = {2376-5992},
    doi = {10.7717/peerj-cs.74},
    abstract = {Summary. Differential Evolution Entirely Parallel (DEEP) package is a software for finding unknown real
    and integer parameters in dynamical models of biological processes by minimizing one or even several objective
    functions that measure the deviation of model solution from data. Numerical solutions provided by the most
    efficient global optimization methods are often problem-specific and cannot be easily adapted to other tasks. In
    contrast, DEEP allows a user to describe both mathematical model and objective function in any programming
    language, such as R, Octave or Python and others. Being implemented in C, DEEP demonstrates as good performance as
    the top three methods from CEC-2014 (Competition on evolutionary computation) benchmark and was successfully
    applied to several biological problems.},
    journal = {PeerJ Computer Science},
    keywords = {Bioinformatics, Differential Evolution, Mathematical modeling, 
    Open source software,Parallelization,Parameter optimization},
    language = {en}
}

@article{Kozlov13,
 author = {Konstantin Kozlov and Nikita Ivanisenko and Vladimir Ivanisenko and Nikolay Kolchanov and Maria Samsonova and Alexander M. Samsonov},
 title = {{Enhanced Differential Evolution Entirely Parallel Method for Biomedical Applications}},
 journal = {{LNCS 7979}},
 volume = {V. Malyshkin (Ed.): PaCT 2013},
 pages = {409--416},
 year = {2013}
}

@article{Kozlov11,
 author = {Konstantin Kozlov and Alexander Samsonov},
 title = {DEEP -- Differential Evolution Entirely
 Parallel Method for Gene Regulatory Networks},
 journal = {Journal of Supercomputing},
 year = {2011},
 volume = {57},
 pages = {172-178}
}

Get the sources:

git clone git://git.code.sf.net/p/deepmethod/code deepmethod-code

To update the source tree to the latest you'll need to:

cd deep
git pull

Prerequisites:

glib-2.0 >= 2.22
gthread-2.0 >= 2.22
gio-2.0 >= 2.22

Build:

cd deep
autoreconf -fi
./configure --prefix=/usr/local
make
su -c 'make install'

Packages

RPM SPEC is available at github

Packages listed in the Files section in RPMS directory are build in Open SUSE Build service and are accessible from the following YUM repositories:

For Fedora 21 run the following as root:

cd /etc/yum.repos.d/
wget http://download.opensuse.org/repositories/home:mackoel:compbio/Fedora_21\
/home:mackoel:compbio.repo
yum install deepmethod

You can change "Fedora_21" to "CentOS_7", "CentOS_6", "Fedora_20", "RHEL_7" or "RHEL_6" to get package for your OS.