mcga

Machine Coded Genetic Algorithms for Real-Valued Optimization Problems

Last update: 27 November 2023

Copyright license: GNU General Public License, version 3.0, GNU General Public License, version 2.0

Software version identifier: 3.0.3, 1.0, 1.1, 1.2.1, 1.2, 2.0.1, 2.0.2, 2.0.3, 2.0.4, 2.0.5, 2.0.6, 2.0.7, 2.0.9, 2.0, 3.0.1, 3.0.6, 3.0, 3.0.7

Source code repository: https://github.com/cran/mcga

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.

This page was built for software: mcga