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.


News

Reference manual

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.

install.packages("mcga")

3.0.3 by Mehmet Hakan Satman, 8 months ago


Browse source code at https://github.com/cran/mcga


Authors: Mehmet Hakan Satman


Documentation:   PDF Manual  


Task views: Optimization and Mathematical Programming


GPL (>= 2) license


Imports Rcpp

Depends on GA

Linking to Rcpp


See at CRAN