Learn R Programming

STPGA (version 5.2.1)

Selection of Training Populations by Genetic Algorithm

Description

Combining Predictive Analytics and Experimental Design to Optimize Results. To be utilized to select a test data calibrated training population in high dimensional prediction problems and assumes that the explanatory variables are observed for all of the individuals. Once a "good" training set is identified, the response variable can be obtained only for this set to build a model for predicting the response in the test set. The algorithms in the package can be tweaked to solve some other subset selection problems.

Copy Link

Version

Install

install.packages('STPGA')

Monthly Downloads

219

Version

5.2.1

License

GPL-3

Maintainer

Last Published

November 24th, 2018

Functions in STPGA (5.2.1)

STPGA-package

Selection of Training Populations by Genetic Algorithm
disttoideal

Calculate the distance of solutions from the 'ideal' solution.
GenAlgForSubsetSelectionNoTest

Genetic algorithm for subset selection no given test
makeonecross

Make a cross from two solutions and mutate.
Amat.pieces

Amat.pieces
GenAlgForSubsetSelection

Genetic algorithm for subset selection
WheatData

Adult plant height (estimated genetic values) for 1182 elite wheat lines
GenAlgForSubsetSelectionMO

Genetic algorithm for subset selection no given test with multiple criteria for Multi Objective Optimized Experimantal Design.
GenAlgForSubsetSelectionMONoTest

Genetic algorithm for subset selection no given test with multiple criteria for Multi Objective Optimized Experimental Design.
GenerateCrossesfromElites

Generate crosses from elites
CRITERIA

Optimality Criteria