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: |
3.0.3 |
| Depends: |
GA |
| Imports: |
Rcpp (≥ 0.11.4) |
| LinkingTo: |
Rcpp |
| Published: |
2018-05-13 |
| Author: |
Mehmet Hakan Satman |
| Maintainer: |
Mehmet Hakan Satman <mhsatman at istanbul.edu.tr> |
| License: |
GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: |
yes |
| Citation: |
mcga citation info |
| In views: |
Optimization |
| CRAN checks: |
mcga results |
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