tag:crantastic.org,2005:/authors/1351Latest activity for Mehmet Hakan Satman2018-05-16T22:00:59Zcrantastic.orgtag:crantastic.org,2005:TimelineEvent/751062018-05-16T22:00:59Z2018-05-16T22:00:59Zeive was upgraded to version 2.3<a href="/packages/eive">eive</a> was <span class="action">upgraded</span> to version <a href="/packages/eive/versions/71708">2.3</a><br /><h3>Package description:</h3><p>Performs a compact genetic algorithm search to reduce errors-in-variables bias in linear regression. The algorithm estimates the regression parameters with lower biases and higher variances but mean-square errors (MSEs) are reduced.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/749302018-05-13T20:41:39Z2018-05-13T20:41:39Zmcga was upgraded to version 3.0.3<a href="/packages/mcga">mcga</a> was <span class="action">upgraded</span> to version <a href="/packages/mcga/versions/71532">3.0.3</a><br /><h3>Package description:</h3><p>Machine coded genetic algorithm (MCGA) is a fast tool for real-valued optimization problems. It uses the byte representation of variables rather than real-values. It performs the classical crossover operations (uniform) on these byte representations. Mutation operator is also similar to classical mutation operator, which is to say, it changes a randomly selected byte value of a chromosome by +1 or -1 with probability 1/2. In MCGAs there is no need for encoding-decoding process and the classical operators are directly applicable on real-values. It is fast and can handle a wide range of a search space with high precision. Using a 256-unary alphabet is the main disadvantage of this algorithm but a moderate size population is convenient for many problems. Package also includes multi_mcga function for multi objective optimization problems. This function sorts the chromosomes using their ranks calculated from the non-dominated sorting algorithm.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/688152017-11-24T11:00:54Z2017-11-24T11:00:54Zgalts was upgraded to version 1.3.1<a href="/packages/galts">galts</a> was <span class="action">upgraded</span> to version <a href="/packages/galts/versions/65795">1.3.1</a><br /><h3>Package description:</h3><p>Includes the ga.lts() function that estimates LTS (Least Trimmed Squares) parameters using genetic algorithms and C-steps. ga.lts() constructs a genetic algorithm to form a basic subset and iterates C-steps as defined in Rousseeuw and van-Driessen (2006) to calculate the cost value of the LTS criterion. OLS (Ordinary Least Squares) regression is known to be sensitive to outliers. A single outlying observation can change the values of estimated parameters. LTS is a resistant estimator even the number of outliers is up to half of the data. This package is for estimating the LTS parameters with lower bias and variance in a reasonable time. Version >=1.3 includes the function medmad for fast outlier detection in linear regression.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/687882017-11-23T12:00:59Z2017-11-23T12:00:59Zforega was upgraded to version 1.0.3<a href="/packages/forega">forega</a> was <span class="action">upgraded</span> to version <a href="/packages/forega/versions/65768">1.0.3</a><br /><h3>Package description:</h3><p>The implemented algorithm performs a floating-point genetic algorithm search with a statistical forecasting operator that generates offspring which probably will be generated in future generations. Use of this operator enhances the search capabilities of floating-point genetic algorithms because offspring generated by usual genetic operators rapidly forecasted before performing more generations.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/666872017-09-22T17:20:56Z2017-09-22T17:20:56Zforega was upgraded to version 1.0.2<a href="/packages/forega">forega</a> was <span class="action">upgraded</span> to version <a href="/packages/forega/versions/63787">1.0.2</a><br /><h3>Package description:</h3><p>The implemented algorithm performs a floating-point genetic algorithm search with a statistical forecasting operator that generates offspring which probably will be generated in future generations. Use of this operator enhances the search capabilities of floating-point genetic algorithms because offspring generated by usual genetic operators rapidly forecasted before performing more generations.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/511532016-05-12T14:40:54Z2016-05-12T14:40:54Zmcga was upgraded to version 3.0.1<a href="/packages/mcga">mcga</a> was <span class="action">upgraded</span> to version <a href="/packages/mcga/versions/49430">3.0.1</a><br /><h3>Package description:</h3><p>Machine coded genetic algorithm (MCGA) is a fast tool for real-valued optimization problems. It uses the byte representation of variables rather than real-values. It performs the classical crossover operations (uniform) on these byte representations. Mutation operator is also similar to classical mutation operator, which is to say, it changes a randomly selected byte value of a chromosome by +1 or -1 with probability 1/2. In MCGAs there is no need for encoding-decoding process and the classical operators are directly applicable on real-values. It is fast and can handle a wide range of a search space with high precision. Using a 256-unary alphabet is the main disadvantage of this algorithm but a moderate size population is convenient for many problems. Package also includes multi_mcga function for multi objective optimization problems. This function sorts the chromosomes using their ranks calculated from the non-dominated sorting algorithm.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/497692016-03-24T23:21:00Z2016-03-24T23:21:00Zmcga was upgraded to version 3.0<a href="/packages/mcga">mcga</a> was <span class="action">upgraded</span> to version <a href="/packages/mcga/versions/48128">3.0</a><br /><h3>Package description:</h3><p>Machine coded genetic algorithm (MCGA) is a fast tool for real-valued optimization problems. It uses the byte representation of variables rather than real-values. It performs the classical crossover operations (uniform) on these byte representations. Mutation operator is also similar to classical mutation operator, which is to say, it changes a randomly selected byte value of a chromosome by +1 or -1 with probability 1/2. In MCGAs there is no need for encoding-decoding process and the classical operators are directly applicable on real-values. It is fast and can handle a wide range of a search space with high precision. Using a 256-unary alphabet is the main disadvantage of this algorithm but a moderate size population is convenient for many problems. Package also includes multi_mcga function for multi objective optimization problems. This function sorts the chromosomes using their ranks calculated from the non-dominated sorting algorithm.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/487532016-03-11T20:15:32Z2016-03-11T20:15:32Zcrantastic_production tagged mcga with Optimization<a href="/users/146">crantastic_production</a> <span class="action">tagged</span> <a href="/packages/mcga">mcga</a> with <a href="/task_views/Optimization">Optimization</a>crantastic_productiontag:crantastic.org,2005:TimelineEvent/462102016-01-02T18:10:56Z2016-01-02T18:10:56Zforega was released<a href="/packages/forega">forega</a> was <span class="action">released</span><br /><h3>Package description:</h3><p>The implemented algorithm performs a floating-point genetic algorithm search with a statistical forecasting operator that generates offspring which probably will be generated in future generations. Use of this operator enhances the search capabilities of floating-point genetic algorithms because offspring generated by usual genetic operators rapidly forecasted before performing more generations.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/314272013-04-04T19:10:37Z2013-04-04T19:10:37Zmcga was upgraded to version 2.0.7<a href="/packages/mcga">mcga</a> was <span class="action">upgraded</span> to version <a href="/packages/mcga/versions/26666">2.0.7</a><br /><h3>Package description:</h3><p>Machine coded genetic algorithm (MCGA) is a fast tool for real-valued optimization problems. It uses the byte representation of variables rather than real-values. It performs the classical crossover operations (uniform) on these byte representations. Mutation operator is also similar to classical mutation operator, which is to say, it changes a randomly selected byte value of a chromosome by +1 or -1 with probability 1/2. In MCGAs there is no need for encoding-decoding process and the classical operators are directly applicable on real-values. It is fast and can handle a wide range of a search space with high precision. Using a 256-unary alphabet is the main disadvantage of this algorithm but a moderate size population is convenient for many problems. Package also includes multi_mcga function for multi objective optimization problems. This function sorts the chromosomes using their ranks calculated from the non-dominated sorting algorithm.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/291892013-02-07T08:30:29Z2013-02-07T08:30:29Zgalts was upgraded to version 1.3<a href="/packages/galts">galts</a> was <span class="action">upgraded</span> to version <a href="/packages/galts/versions/24966">1.3</a><br /><h3>Package description:</h3><p>This package includes the ga.lts function that estimates LTS (Least Trimmed Squares) parameters using genetic algorithms and C-steps. ga.lts() constructs a genetic algorithm to form a basic subset and iterates C-steps as defined in Rousseeuw and van-Driessen (2006) to calculate the cost value of the LTS criterion. OLS(Ordinary Least Squares) regression is known to be sensitive to outliers. A single outlying observation can change the values of estimated parameters. LTS is a resistant estimator even the number of outliers is up to half of the data. This package is for estimating the LTS parameters with lower bias and variance in a reasonable time. Version 1.3 included the function medmad for fast outlier detection in linear regression.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/275252012-12-07T21:50:22Z2012-12-07T21:50:22Zeive was released<a href="/packages/eive">eive</a> was <span class="action">released</span><br /><h3>Package description:</h3><p>Performs a compact genetic algorithm search to reduce errors-in-variables bias in linear regression. The algorithm estimates the regression parameters with lower biases and higher variances but mean-square errors (MSEs) are reduced.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/265142012-10-31T09:50:45Z2012-10-31T09:50:45Zmcga was upgraded to version 2.0.6<a href="/packages/mcga">mcga</a> was <span class="action">upgraded</span> to version <a href="/packages/mcga/versions/22416">2.0.6</a><br /><h3>Package description:</h3><p>Machine coded genetic algorithm (MCGA) is a fast tool for real-valued optimization problems. It uses the byte representation of variables rather than real-values. It performs the classical crossover operations (uniform) on these byte representations. Mutation operator is also similar to classical mutation operator, which is to say, it changes a randomly selected byte value of a chromosome by +1 or -1 with probability 1/2. In MCGAs there is no need for encoding-decoding process and the classical operators are directly applicable on real-values. It is fast and can handle a wide range of a search space with high precision. Using a 256-unary alphabet is the main disadvantage of this algorithm but a moderate size population is convenient for many problems. Package also includes multi_mcga function for multi objective optimization problems. This function sorts the chromosomes using their ranks calculated from the non-dominated sorting algorithm.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/241812012-08-01T06:52:07Z2012-08-01T06:52:07ZRuniversal was upgraded to version 1.0.2<a href="/packages/Runiversal">Runiversal</a> was <span class="action">upgraded</span> to version <a href="/packages/Runiversal/versions/20237">1.0.2</a><br /><h3>Package description:</h3><p>This package contains some functions for converting R objects to Java style variables and XML. Generated Java code is interpretable by dynamic Java libraries such as Beanshell. Calling R externally and handling the Java or XML output is an other way to call R from other languages without native interfaces. For a Java implementation of this approach visit http://www.mhsatman.com/rcaller.php and http://stdioe.blogspot.com/search/label/rcaller</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/187382012-01-10T23:14:18Z2012-01-10T23:14:18Zmcga was upgraded to version 2.0.5<a href="/packages/mcga">mcga</a> was <span class="action">upgraded</span> to version <a href="/packages/mcga/versions/16427">2.0.5</a><br /><h3>Package description:</h3><p>Machine coded genetic algorithm (MCGA) is a fast tool for real-valued optimization problems. It uses the byte representation of variables rather than real-values. It performs the classical crossover operations (uniform) on these byte representations. Mutation operator is also similar to classical mutation operator, which is to say, it changes a randomly selected byte value of a chromosome by +1 or -1 with probability 1/2. In MCGAs there is no need for encoding-decoding process and the classical operators are directly applicable on real-values. It is fast and can handle a wide range of a search space with high precision. Using a 256-unary alphabet is the main disadvantage of this algorithm but a moderate size population is convenient for many problems. Package also includes multi_mcga function for multi objective optimization problems. This function sorts the chromosomes using their ranks calculated from the non-dominated sorting algorithm.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/186682012-01-10T23:12:32Z2012-01-10T23:12:32Zgalts was upgraded to version 1.2<a href="/packages/galts">galts</a> was <span class="action">upgraded</span> to version <a href="/packages/galts/versions/16357">1.2</a><br /><h3>Package description:</h3><p>This package includes the ga.lts function that estimates LTS (Least Trimmed Squares) parameters using genetic algorithms and C-steps. ga.lts() constructs a genetic algorithm to form a basic subset and iterates C-steps as defined in Rousseeuw and van-Driessen (2006) to calculate the cost value of the LTS criterion. OLS(Ordinary Least Squares) regression is known to be sensitive to outliers. A single outlying observation can change the values of estimated parameters. LTS is a resistant estimator even the number of outliers is up to half of the data. This package is for estimating the LTS parameters with lower bias and variance in a reasonable time.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/167192011-10-17T18:50:23Z2011-10-17T18:50:23Zmcga was upgraded to version 2.0.2<a href="/packages/mcga">mcga</a> was <span class="action">upgraded</span> to version <a href="/packages/mcga/versions/14900">2.0.2</a><br /><h3>Package description:</h3><p>Machine coded genetic algorithm (MCGA) is a fast tool for real-valued optimization problems. It uses the byte representation of variables rather than real-values. It performs the classical crossover operations (uniform) on these byte representations. Mutation operator is also similar to classical mutation operator, which is to say, it changes a randomly selected byte value of a chromosome by +1 or -1 with probability 1/2. In MCGAs there is no need for encoding-decoding process and the classical operators are directly applicable on real-values. It is fast and can handle a wide range of a search space with high precision. Using a 256-unary alphabet is the main disadvantage of this algorithm but a moderate size population is convenient for many problems.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/147802011-07-27T09:30:15Z2011-07-27T09:30:15ZSSSR was upgraded to version 1.0.5<a href="/packages/SSSR">SSSR</a> was <span class="action">upgraded</span> to version <a href="/packages/SSSR/versions/13318">1.0.5</a><br /><h3>Package description:</h3><p>A package for writing server side R scripts with session, application and cookie support</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/142312011-06-25T19:10:25Z2011-06-25T19:10:25Zmcga was upgraded to version 2.0.1<a href="/packages/mcga">mcga</a> was <span class="action">upgraded</span> to version <a href="/packages/mcga/versions/12908">2.0.1</a><br /><h3>Package description:</h3><p>Machine coded genetic algorithm (MCGA) is a fast tool for real-valued optimization problems. It uses the byte representation of variables rather than real-values. It performs the classical crossover operations (uniform) on these byte representations. Mutation operator is also similar to classical mutation operator, which is to say, it changes a randomly selected byte value of a chromosome by +1 or -1 with probability 1/2. In MCGAs there is no need for encoding-decoding process and the classical operators are directly applicable on real-values. It is fast and can handle a wide range of a search space with high precision. Using a 256-unary alphabet is the main disadvantage of this algorithm but a moderate size population is convenient for many problems.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/141752011-06-21T13:10:29Z2011-06-21T13:10:29Zmcga was upgraded to version 2.0<a href="/packages/mcga">mcga</a> was <span class="action">upgraded</span> to version <a href="/packages/mcga/versions/12854">2.0</a><br /><h3>Package description:</h3><p>Machine coded genetic algorithm (MCGA) is a fast tool for real-valued optimization problems. It uses the byte representation of variables rather than real-values. It performs the classical crossover operations (uniform) on these byte representations. Mutation operator is also similar to classical mutation operator, which is to say, it changes a randomly selected byte value of a chromosome by +1 or -1 with probability 1/2. In MCGAs there is no need for encoding-decoding process and the classical operators are directly applicable on real-values. It is fast and can handle a wide range of a search space with high precision. Using a 256-unary alphabet is the main disadvantage of this algorithm but a moderate size population is convenient for many problems.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/112862011-02-05T17:30:26Z2011-02-05T17:30:26Zgalts was upgraded to version 1.1<a href="/packages/galts">galts</a> was <span class="action">upgraded</span> to version <a href="/packages/galts/versions/10546">1.1</a><br /><h3>Package description:</h3><p>This package includes the ga.lts function that estimates LTS (Least Trimmed Squares) parameters using genetic algorithms and C-steps. ga.lts() constructs a genetic algorithm to form a basic subset and iterates C-steps as defined in Rousseeuw and van-Driessen (2006) to calculate the cost value of the LTS criterion. OLS(Ordinary Least Squares) regression is known to be sensitive to outliers. A single outlying observation can change the values of estimated parameters. LTS is a resistant estimator even the number of outliers is up to half of the data. This package is for estimating the LTS parameters with lower bias and variance in a reasonable time.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/107582011-01-11T19:10:17Z2011-01-11T19:10:17Zmcga was upgraded to version 1.2.1<a href="/packages/mcga">mcga</a> was <span class="action">upgraded</span> to version <a href="/packages/mcga/versions/10209">1.2.1</a><br /><h3>Package description:</h3><p>Machine Coded Genetic Algorithms (mcga) builds genetic algorithms with machine coded chromosomes for real value optimizitation problems. Chromosomes are not encoded/decoded, the byte representation of double values are used for the genetic operations such as crossing-over and mutation. Computers already decode and encode the variables using a finite set of alphabet. This makes mcga extremely fast even the number of parameters is high.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/107532011-01-11T11:50:21Z2011-01-11T11:50:21Zmcga was upgraded to version 1.2<a href="/packages/mcga">mcga</a> was <span class="action">upgraded</span> to version <a href="/packages/mcga/versions/10206">1.2</a><br /><h3>Package description:</h3><p>Machine Coded Genetic Algorithms (mcga) builds genetic algorithms with machine coded chromosomes for real value optimizitation problems. Chromosomes are not encoded/decoded, the byte representation of double values are used for the genetic operations such as crossing-over and mutation. Computers already decode and encode the variables using a finite set of alphabet. This makes mcga extremely fast even the number of parameters is high.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/104702010-12-23T11:50:27Z2010-12-23T11:50:27Zmcga was upgraded to version 1.1<a href="/packages/mcga">mcga</a> was <span class="action">upgraded</span> to version <a href="/packages/mcga/versions/10022">1.1</a><br /><h3>Package description:</h3><p>Machine Coded Genetic Algorithms (mcga) builds genetic algorithms with machine coded chromosomes for real value optimizitation problems. Chromosomes are not encoded/decoded, the byte representation of double values are used for the genetic operations such as crossing-over and mutation. Computers already decode and encode the variables using a finite set of alphabet. This makes mcga extremely fast even the number of parameters is high.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/103342010-12-13T14:30:34Z2010-12-13T14:30:34Zmcga was released<a href="/packages/mcga">mcga</a> was <span class="action">released</span><br /><h3>Package description:</h3><p>Machine coded genetic algorithm (MCGA) is a fast tool for real-valued optimization problems. It uses the byte representation of variables rather than real-values. It performs the classical crossover operations (uniform) on these byte representations. Mutation operator is also similar to classical mutation operator, which is to say, it changes a randomly selected byte value of a chromosome by +1 or -1 with probability 1/2. In MCGAs there is no need for encoding-decoding process and the classical operators are directly applicable on real-values. It is fast and can handle a wide range of a search space with high precision. Using a 256-unary alphabet is the main disadvantage of this algorithm but a moderate size population is convenient for many problems. Package also includes multi_mcga function for multi objective optimization problems. This function sorts the chromosomes using their ranks calculated from the non-dominated sorting algorithm.</p>crantastic.org