Title: Machine Coded Genetic Algorithms for the Real Value Optimization
Description: 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

Package: mcga
Type: Package
Title: Machine coded genetic algorihms for real-valued optimization
problems
Version: 2.0.1
Date: 2011-06-25
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.
License: GPL
LazyLoad: yes
Packaged: 2011-06-25 13:23:23 UTC; hako
Repository: CRAN
Date/Publication: 2011-06-25 18:49:13

Package: mcga
Type: Package
Title: Machine coded genetic algorihms for real-valued optimization
problems
Version: 2.0.2
Date: 2011-10-17
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.
License: GPL
LazyLoad: yes
Repository: CRAN
Date/Publication: 2011-10-17 18:41:45
Packaged: 2011-10-17 07:50:43 UTC; hako