Package: genetic.algo.optimizeR 0.3.2

genetic.algo.optimizeR: Genetic Algorithm Optimization

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 an optimization problem that involves finding the best solution in a discrete space.

Authors:Dany Mukesha [aut, cre]

genetic.algo.optimizeR_0.3.2.tar.gz
genetic.algo.optimizeR_0.3.2.zip(r-4.7-any)genetic.algo.optimizeR_0.3.2.zip(r-4.6-any)genetic.algo.optimizeR_0.3.2.zip(r-4.5-any)
genetic.algo.optimizeR_0.3.2.tgz(r-4.6-any)genetic.algo.optimizeR_0.3.2.tgz(r-4.5-any)
genetic.algo.optimizeR_0.3.2.tar.gz(r-4.7-any)genetic.algo.optimizeR_0.3.2.tar.gz(r-4.6-any)
genetic.algo.optimizeR_0.3.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
genetic.algo.optimizeR/json (API)

# Install 'genetic.algo.optimizeR' in R:
install.packages('genetic.algo.optimizeR', repos = c('https://danymukesha.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/danymukesha/genetic.algo.optimizer/issues

Pkgdown/docs site:https://danymukesha.github.io

On CRAN:

Conda:

experimentaldesigntechnology

4.04 score 11 scripts 534 downloads 6 exports 90 dependencies

Last updated from:1a2a1a1899. Checks:8 OK, 1 ERROR. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK214
source / vignettesOK249
linux-releaseOK174
macos-releaseOK200
macos-oldrelOK264
windows-develOK103
windows-releaseOK131
windows-oldrelERROR133
wasm-releaseOK130

Exports:crossoverevaluate_fitnessinitialize_populationmutationreplacementselection

Dependencies:askpassbase64encBHBiobaseBiocGenericsBiocManagerbiocViewsbitbit64bitopsbslibcachemclicliprcpp11crayoncurlDiagrammeRdigestdplyrevaluatefarverfastmapfontawesomefsgenericsggplot2gluegraphgtablehighrhmshtmltoolshtmlwidgetshttr2igraphisobandjosejquerylibjsonliteknitrlabelinglatticelifecyclemagrittrMatrixmemoisemimeopensslpackratpillarpkgconfigprettyunitsprogresspurrrR6rappdirsRBGLRColorBrewerRcppRcppTOMLRCurlreadrrenvrlangrmarkdownrsconnectrstudioapiRUnitS7sassscalessnowflakeauthstringistringrsystibbletidyrtidyselecttinytextzdbutf8vctrsviridisLitevisNetworkvroomwithrxfunXMLyaml

Optimization of a Quadratic Function Using Genetic Algorithms
Abstract | Introduction | Demonstration | Initialization of population | Fitness Evaluation | Selection of Parents | Crossover and Mutation | Replacement | Iteration Until Termination | Finding the Optimal Solution | Analysis of X-Intercepts | Conclusion
Rendered fromoptimize_function_with_GA.Rmdusingknitr::rmarkdown

Last update: 2024-10-07
Started: 2024-10-07

Introduction to genetic.algo.optimizeR
Overview | Aim | Method | Initial Population | Evaluation | Selection | Crossover and Mutation | Replacement | Warp-Up
Rendered fromintro_to_genetic.algo.optimizeR.Rmdusingknitr::rmarkdown

Last update: 2024-10-07
Started: 2024-10-07