Package: BioGA 0.99.6

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]

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BioGA.pdf |BioGA.html✨
BioGA/json (API)
NEWS

# 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

Uses libs:

c++– GNU Standard C++ Library v3

On CRAN:

9 exports 1.08 score 87 dependencies

Last updated 1 months agofrom:037f6cb3b1

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 askpass base64enc BH Biobase BiocGenerics BiocManager BiocStyle biocViews bitops bookdown bslib cachem cli colorspace crayon curl DelayedArray digest evaluate fansi farver fastmap fontawesome fs GenomeInfoDb GenomeInfoDbData GenomicRanges ggplot2 glue graph gtable highr htmltools httr IRanges isoband jquerylib jsonlite knitr labeling lattice lifecycle magick magrittr MASS Matrix MatrixGenerics matrixStats memoise mgcv mime munsell nlme openssl pillar pkgconfig R6 rappdirs RBGL RColorBrewer Rcpp RCurl rlang rmarkdown RUnit S4Arrays S4Vectors sass scales sessioninfo SparseArray SummarizedExperiment sys tibble tinytex UCSC.utils utf8 vctrs viridisLite withr xfun XML XVector yaml zlibbioc

Introduction

Dany Mukesha

Rendered fromIntroduction.Rmdusingknitr::rmarkdownon Jun 16 2024.

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