R-universe search: exports:mutation

Showing 7 of total 7 results (show query)

green-striped-gecko

PopGenReport:A Simple Framework to Analyse Population and Landscape Genetic Data

Provides beginner friendly framework to analyse population genetic data. Based on 'adegenet' objects it uses 'knitr' to create comprehensive reports on spatial genetic data. For detailed information how to use the package refer to the comprehensive tutorials or visit <http://www.popgenreport.org/>.

Maintained by Bernd Gruber. Last updated 1 years ago.

5 stars 7.27 score 82 scripts 1 dependents

ysosirius

windfarmGA:Genetic Algorithm for Wind Farm Layout Optimization

The genetic algorithm is designed to optimize wind farms of any shape. It requires a predefined amount of turbines, a unified rotor radius and an average wind speed value for each incoming wind direction. A terrain effect model can be included that downloads an 'SRTM' elevation model and loads a Corine Land Cover raster to approximate surface roughness.

Maintained by Sebastian Gatscha. Last updated 2 months ago.

windfarm-layout optimization genetic-algorithm renewable-energy cpp

29 stars 4.99 score 17 scripts

mli171

changepointGA:Changepoint Detection via Modified Genetic Algorithm

The Genetic Algorithm (GA) is used to perform changepoint analysis in time series data. The package also includes an extended island version of GA, as described in Lu, Lund, and Lee (2010, <doi:10.1214/09-AOAS289>). By mimicking the principles of natural selection and evolution, GA provides a powerful stochastic search technique for solving combinatorial optimization problems. In 'changepointGA', each chromosome represents a changepoint configuration, including the number and locations of changepoints, hyperparameters, and model parameters. The package employs genetic operators—selection, crossover, and mutation—to iteratively improve solutions based on the given fitness (objective) function. Key features of 'changepointGA' include encoding changepoint configurations in an integer format, enabling dynamic and simultaneous estimation of model hyperparameters, changepoint configurations, and associated parameters. The detailed algorithmic implementation can be found in the package vignettes and in the paper of Li (2024, <doi:10.48550/arXiv.2410.15571>).

Maintained by Mo Li. Last updated 1 months ago.

cpp

4.95 score

rdinnager

slimr:Create, Run and Post-Process 'SLiM' Population Genetics Forward Simulations

Lets you write 'SLiM' scripts (population genomics simulation) using your favourite R IDE, using a syntax as close as possible to the original 'SLiM' language. It offer many tools to manipulate those scripts, as well as run them in the 'SLiM' software from R, as well as capture and post-process their output, after or even during a simulation.

Maintained by Russell Dinnage. Last updated 5 months ago.

8 stars 4.70 score 42 scripts

danymukesha

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.

Maintained by Dany Mukesha. Last updated 6 months ago.

experimentaldesign technology

4.30 score 10 scripts

bradduthie

gamesGA:Genetic Algorithm for Sequential Symmetric Games

Finds adaptive strategies for sequential symmetric games using a genetic algorithm. Currently, any symmetric two by two matrix is allowed, and strategies can remember the history of an opponent's play from the previous three rounds of moves in iterated interactions between players. The genetic algorithm returns a list of adaptive strategies given payoffs, and the mean fitness of strategies in each generation.

Maintained by A. Bradley Duthie. Last updated 5 years ago.

game game-theory genetic-algorithm machine-learning prisoner-dilemma-simulation

3 stars 3.22 score 11 scripts

cran

Ease:Simulating Explicit Population Genetics Models

Implementation in a simple and efficient way of fully customisable population genetics simulations, considering multiple loci that have epistatic interactions. Specifically suited to the modelling of multilocus nucleocytoplasmic systems (with both diploid and haploid loci), it is nevertheless possible to simulate purely diploid (or purely haploid) genetic models. Examples of models that can be simulated with Ease are numerous, for example models of genetic incompatibilities as presented by Marie-Orleach et al. (2022) <doi:10.1101/2022.07.25.501356>. Many others are conceivable, although few are actually explored, Ease having been developed in particular to provide a solution so that these kinds of models can be simulated simply.

Maintained by Ehouarn Le Faou. Last updated 2 years ago.

cpp

2.00 score