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: 3.0.7
Depends: GA
Imports: Rcpp (≥ 0.11.4)
LinkingTo: Rcpp
Published: 2023-11-27
DOI: 10.32614/CRAN.package.mcga
Author: Mehmet Hakan Satman
Maintainer: Mehmet Hakan Satman <mhsatman at>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: yes
Citation: mcga citation info
In views: Optimization
CRAN checks: mcga results


Reference manual: mcga.pdf


Package source: mcga_3.0.7.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): mcga_3.0.7.tgz, r-oldrel (arm64): mcga_3.0.7.tgz, r-release (x86_64): mcga_3.0.7.tgz, r-oldrel (x86_64): mcga_3.0.7.tgz
Old sources: mcga archive


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