Recent changes to Home

Home modified by KKozlov

KKozlov — Wed, 21 Oct 2020 08:46:57 -0000

--- v17
+++ v18
@@ -23,18 +23,26 @@
 ## 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}
+        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,

Home modified by KKozlov

KKozlov — Wed, 21 Oct 2020 08:45:20 -0000

--- v16
+++ v17
@@ -22,6 +22,30 @@

 ## 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
@@ -32,30 +56,6 @@
      pages = {172-178}
     }

-    @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{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.},
-  copyright = {All rights reserved},
-  journal = {PeerJ Computer Science},
-  keywords = {Bioinformatics,Differential Evolution,Mathematical modeling,Open source software,Parallelization,Parameter optimization},
-  language = {en}
-}

 ## Get the sources:

Home modified by KKozlov

KKozlov — Wed, 21 Oct 2020 08:41:49 -0000

--- v15
+++ v16
@@ -41,6 +41,21 @@
      year = {2013}
     }

+    @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.},
+  copyright = {All rights reserved},
+  journal = {PeerJ Computer Science},
+  keywords = {Bioinformatics,Differential Evolution,Mathematical modeling,Open source software,Parallelization,Parameter optimization},
+  language = {en}
+}

 ## Get the sources:

Home modified by KKozlov

KKozlov — Tue, 17 May 2016 12:40:49 -0000

--- v14
+++ v15
@@ -91,6 +91,7 @@
   * [ExternalModelParameters]
   * [CommandLineArguments]
   * [InteroperabilityWithOtherSystems]
+  * [Application Program Interfaces]
   * [ЧастоЗадаваемыеВопросы](ЧастоЗадаваемыеВопросы)

 ## Links

Home modified by KKozlov

KKozlov — Fri, 23 Oct 2015 10:34:27 -0000

--- v13
+++ v14
@@ -78,8 +78,8 @@
 For Fedora 21 run the following as root:

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

Home modified by KKozlov

KKozlov — Thu, 19 Feb 2015 12:01:38 -0000

--- v12
+++ v13
@@ -44,7 +44,7 @@

 ## Get the sources:

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

 To update the source tree to the latest you'll need to: 
@@ -91,7 +91,7 @@
   * [ExternalModelParameters]
   * [CommandLineArguments]
   * [InteroperabilityWithOtherSystems]
-  * [ЧастоЗадаваемыеВопросы](ЧастоЗадаваемыеВопрос)
+  * [ЧастоЗадаваемыеВопросы](ЧастоЗадаваемыеВопросы)

 ## Links

Home modified by KKozlov

KKozlov — Thu, 19 Feb 2015 12:01:07 -0000

--- v11
+++ v12
@@ -44,7 +44,7 @@

 ## Get the sources:

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

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

Home modified by KKozlov

KKozlov — Thu, 19 Feb 2015 12:00:26 -0000

--- v10
+++ v11
@@ -44,7 +44,7 @@

 ## Get the sources:

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

 To update the source tree to the latest you'll need to: 
@@ -78,7 +78,7 @@
 For Fedora 21 run the following as root:

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

Home modified by KKozlov

KKozlov — Thu, 19 Feb 2015 11:59:46 -0000

--- v9
+++ v10
@@ -44,7 +44,7 @@

 ## Get the sources:

-    `git clone git://git.code.sf.net/p/deepmethod/code deepmethod-code`
+    git clone git\://git.code.sf.net/p/deepmethod/code deepmethod-code

 To update the source tree to the latest you'll need to: 
@@ -77,10 +77,10 @@

 For Fedora 21 run the following as root:

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

Home modified by KKozlov

KKozlov — Thu, 19 Feb 2015 11:57:29 -0000

--- v8
+++ v9
@@ -44,7 +44,7 @@

 ## Get the sources:

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

 To update the source tree to the latest you'll need to: 
@@ -77,10 +77,10 @@

 For Fedora 21 run the following as root:

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