Package: mcga
Type: Package
Title: Machine Coded Genetic Algorithms for Real-Valued Optimization
Problems
Version: 3.0.3
Date: 2018-05-12
Author: Mehmet Hakan Satman
Maintainer: Mehmet Hakan Satman <mhsatman@istanbul.edu.tr>
Description: 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.
License: GPL (>= 2)
Depends: GA
Imports: Rcpp (>= 0.11.4)
LinkingTo: Rcpp
NeedsCompilation: yes
LazyLoad: yes
Repository: CRAN
Date/Publication: 2018-05-13 20:30:12 UTC
Packaged: 2018-05-13 20:16:47 UTC; istar1
RoxygenNote: 5.0.1

Package: mcga
Type: Package
Title: Machine Coded Genetic Algorithms for Real-Valued Optimization
Problems
Version: 3.0
Date: 2016-02-25
Author: Mehmet Hakan Satman
Maintainer: Mehmet Hakan Satman <mhsatman@istanbul.edu.tr>
Description: 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.
License: GPL (>= 2)
Depends: GA
Imports: Rcpp (>= 0.11.4)
LinkingTo: Rcpp
NeedsCompilation: yes
LazyLoad: yes
Repository: CRAN
Date/Publication: 2016-03-25 00:05:44
Packaged: 2016-03-24 19:50:33 UTC; hako
RoxygenNote: 5.0.1