GenAlgForSubsetSelectionMONoTest: Genetic algorithm for subset selection no given test with...

In STPGA: Selection of Training Populations by Genetic Algorithm

Description

It uses a nondominated selection genetic algorithm to find the solutions on the frontier that optimizes several design criteria at the same time. The test set is taken as the complement of the training individuals.

Usage

GenAlgForSubsetSelectionMONoTest(Pcs = NULL, Dist = NULL, Kernel = NULL, Candidates,
                 ntoselect, selectionstats, selectionstatstypes,
                 plotdirections, npopGA = 100, mutprob = 0.8,
                 mutintensity = 1, nitGA = 500, lambda = 1e-06,
                 plotiters = FALSE, mc.cores = 1, InitPop = NULL, C =
                 NULL, axes.labels = NULL)

Arguments

Pcs	Principal components matrix for the individuals
Dist	Distance matrix for the individuals
Kernel	Kernel matrix for the individuals
Candidates	The set of individuals from which the training set is selected.
ntoselect	number of individuals to select in the training set.
selectionstats	a vector of design optimization criteria
selectionstatstypes	a vector describing the type of optimality criteria used in selectionstats.
plotdirections	A vector that is used to change the sign of statistics while plotting. This doesnt effect the optimization.
npopGA	genetic algorithm parameter, number of solutions at each iteration
mutprob	genetic algorithm parameter, probability of mutation for each generated solution.
mutintensity	genetic algorithm parameter, mean of the poisson variable that is used to decide the number of mutations for each cross.
nitGA	genetic algorithm parameter, number of iterations.
lambda	scalar shrinkage parameter (λ>0).
plotiters	plot the convergence: TRUE or FALSE. Default is TRUE.
mc.cores	number of cores to use.
InitPop	a list of initial solutions
C	Contrast Matrix.
axes.labels	Labels for the axes for plotting iterations

Value

A list of length 2. The first item in the list is the list of solutions found by the algorithm. The second item is a matrix of criteria values for the solutions in the first list.

Author(s)

Deniz Akdemir

Examples

## Not run: 
library(STPGA)
library(GenomicMating)

data(WheatData)


Msvd<-svd(scale(Wheat.M, scale=F, center=T), nu=50, nv=50)
Dgeno<-as.matrix(dist(scale(Wheat.M, scale=F, center=T)))^2
P<-Wheat.M%*%Msvd$v
dim(P)
rownames(Dgeno)<-colnames(Dgeno)<-rownames(P)<-rownames(Wheat.M)
test<-sample(rownames(P), 25)
candidates<-setdiff(rownames(P), test)
outnewprog<-GenAlgForSubsetSelectionMONoTest(Pcs=P,Dist=Dgeno,
Candidates=candidates,ntoselect=75, 
selectionstats=list("DOPT", "neg_dist_in_train2", "dist_to_test2"),
selectionstatstypes=c("Pcs", "Dist", "Dist"),
plotdirections=c(1,1,1),npopGA=300,
mutprob=1, mutintensity=2, nitGA=100,
plotiters=TRUE, mc.cores=1, InitPop=NULL)

#####Best solution according to ideal solution concept
outnewprog[[1]][[which.min(disttoideal(outnewprog[[2]]))]]


## End(Not run)

STPGA documentation built on May 2, 2019, 8:19 a.m.