Package: mcga
Type: Package
Title: Machine coded genetic algorithms for real-valued optimization
problems
Version: 2.0.3
Date: 2012-01-06
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
LazyLoad: yes
Repository: CRAN
Date/Publication: 2012-01-06 20:56:26
Packaged: 2012-01-06 18:25:07 UTC; hako

Package: mcga
Type: Package
Title: Machine coded genetic algorithms for real-valued optimization
problems
Version: 2.0.4
Date: 2012-01-06
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
LazyLoad: yes
Repository: CRAN
Date/Publication: 2012-01-06 21:02:41
Packaged: 2012-01-06 20:56:43 UTC; hako