mcga: Machine coded genetic algorithms for real-valued optimization problems

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.

Version: 2.0.9
Published: 2014-03-25
Author: Mehmet Hakan Satman
Maintainer: Mehmet Hakan Satman <mhsatman at istanbul.edu.tr>
License: GPL-2 | GPL-3 [expanded from: GPL]
NeedsCompilation: yes
In views: Optimization
CRAN checks: mcga results

Downloads:

Reference manual: mcga.pdf
Package source: mcga_2.0.9.tar.gz
OS X binary: mcga_2.0.9.tgz
Windows binary: mcga_2.0.9.zip
Old sources: mcga archive