Package: BioGA 1.7.0

Dany Mukesha

BioGA: Bioinformatics Genetic Algorithm (BioGA)

Genetic algorithm are a class of optimization algorithms inspired by the process of natural selection and genetics. This package allows users to analyze and optimize high throughput genomic data using genetic algorithms. The functions provided are implemented in C++ for improved speed and efficiency, with an easy-to-use interface for use within R.

Authors:Dany Mukesha [aut, cre]

BioGA_1.7.0.tar.gz
BioGA_1.7.0.zip(r-4.7)BioGA_1.7.0.zip(r-4.6)BioGA_1.7.0.zip(r-4.5)
BioGA_1.7.0.tgz(r-4.6-x86_64)BioGA_1.7.0.tgz(r-4.6-arm64)BioGA_1.7.0.tgz(r-4.5-x86_64)BioGA_1.7.0.tgz(r-4.5-arm64)
BioGA_1.7.0.tar.gz(r-4.7-arm64)BioGA_1.7.0.tar.gz(r-4.7-x86_64)BioGA_1.7.0.tar.gz(r-4.6-arm64)BioGA_1.7.0.tar.gz(r-4.6-x86_64)
BioGA_1.7.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
BioGA/json (API)

# Install 'BioGA' in R:

install.packages('BioGA', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/danymukesha/bioga/issues

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

Uses libs:

c++– GNU Standard C++ Library v3

On BioConductor:BioGA-1.7.0(bioc 3.24)BioGA-1.6.0(bioc 3.23)

experimentaldesign technology data-analysis gene-expression genetic-algorithms genomics optimization-algorithms cpp

4.11 score 13 scripts 216 downloads 9 exports 72 dependencies

Last updated from:4a2de1d1e3. Checks:1 WARNING, 13 OK. Indexed: yes.

Target	Result	Time
bioc-checks	WARNING	234
linux-devel-arm64	OK	301
linux-devel-x86_64	OK	388
source / vignettes	OK	344
linux-release-arm64	OK	317
linux-release-x86_64	OK	389
macos-release-arm64	OK	172
macos-release-x86_64	OK	291
macos-oldrel-arm64	OK	162
macos-oldrel-x86_64	OK	335
windows-devel	OK	258
windows-release	OK	287
windows-oldrel	OK	238
wasm-release	OK	192

Exports:crossover_cpp evaluate_fitness_cpp initialize_population_cpp mutation_cpp plot_fitness plot_fitness_history plot_population replacement_cpp selection_cpp

Dependencies:abind animation base64enc BH Biobase BiocGenerics BiocManager BiocStyle biocViews bitops bookdown bslib cachem cli cpp11 curl DelayedArray digest evaluate farver fastmap fontawesome fs generics GenomicRanges ggplot2 glue graph gtable highr htmltools IRanges isoband jquerylib jsonlite knitr labeling lattice lifecycle magick magrittr Matrix MatrixGenerics matrixStats memoise mime R6 rappdirs RBGL RColorBrewer Rcpp RCurl rlang rmarkdown RUnit S4Arrays S4Vectors S7 sass scales Seqinfo sessioninfo SparseArray SummarizedExperiment tinytex vctrs viridisLite withr xfun XML XVector yaml

Introduction

Rendered fromIntroduction.Rmdusingknitr::rmarkdown

Last update: 2024-06-04
Started: 2024-02-26

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Function to perform crossover between selected individuals	crossover_cpp
Function to evaluate fitness using genomic data	evaluate_fitness_cpp
Function to initialize the population from genomic data	initialize_population_cpp
Function to mutate the offspring	mutation_cpp
Plot Fitness Values	plot_fitness
Plot Fitness Change Over Generations	plot_fitness_history
Plot Population Distribution	plot_population
Function to replace non-selected individuals in the population	replacement_cpp
Function to select individuals based on fitness scores	selection_cpp