Genetic algorithm are a class of optimization algorithms inspired by the process of natural selection and genetics. This package is for learning purposes and allows users to optimize various functions or parameters by mimicking biological evolution processes such as selection, crossover, and mutation. Ideal for tasks like machine learning parameter tuning, mathematical function optimization, and solving combinatorial problems.
Version: | 0.2.6 |
Imports: | dplyr, ggplot2, magrittr, rsconnect, stats, stringr, tinytex, biocViews |
Suggests: | BiocStyle, knitr, learnr, rmarkdown, spelling, testthat (≥ 3.0.0) |
Published: | 2024-02-15 |
Author: | Dany Mukesha [aut, cre] |
Maintainer: | Dany Mukesha <danymukesha at gmail.com> |
BugReports: | https://github.com/danymukesha/genetic.algo.optimizeR/issues |
License: | MIT + file LICENSE |
URL: | https://danymukesha.github.io/genetic.algo.optimizeR/, https://github.com/danymukesha/genetic.algo.optimizeR |
NeedsCompilation: | no |
Language: | en-US |
Materials: | README NEWS |
CRAN checks: | genetic.algo.optimizeR results |
Reference manual: | genetic.algo.optimizeR.pdf |
Vignettes: |
Explaining Graph Introduction Theory |
Package source: | genetic.algo.optimizeR_0.2.6.tar.gz |
Windows binaries: | r-devel: genetic.algo.optimizeR_0.2.6.zip, r-release: genetic.algo.optimizeR_0.2.6.zip, r-oldrel: genetic.algo.optimizeR_0.2.6.zip |
macOS binaries: | r-release (arm64): genetic.algo.optimizeR_0.2.6.tgz, r-oldrel (arm64): not available, r-release (x86_64): genetic.algo.optimizeR_0.2.6.tgz, r-oldrel (x86_64): genetic.algo.optimizeR_0.2.6.tgz |
Old sources: | genetic.algo.optimizeR archive |
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