| Title: | Genetic Algorithm with Incomplete Dominance for Feature Selection |
|---|---|
| Description: | Feature selection from high dimensional data using a diploid genetic algorithm with Incomplete Dominance for genotype to phenotype mapping and Random Assortment of chromosomes approach to recombination. |
| Authors: | Nicolae Teodor Melita |
| Maintainer: | Nicolae Teodor Melita <[email protected]> |
| License: | MIT + file LICENSE |
| Version: | 1.2 |
| Built: | 2024-12-21 06:57:51 UTC |
| Source: | CRAN |
Ranks individuals according to their fitness and records the results.
AnalyzeResults(individuals, results, randomAssortment = TRUE, chrConf)AnalyzeResults(individuals, results, randomAssortment = TRUE, chrConf)
individuals |
Population of individuals with diploid genotypes. |
results |
Results returned by EvaluationFunction(). |
randomAssortment |
Random Assortment of Chromosomes for recombinations. The default value is TRUE. |
chrConf |
Configuration of chromosomes returned by splitChromosomes(). |
## Not run: library(genefilter) library(ALL) data(ALL) bALL = ALL[, substr(ALL$BT,1,1) == "B"] smallALL = bALL[, bALL$mol.biol %in% c("BCR/ABL", "NEG")] smallALL$mol.biol = factor(smallALL$mol.biol) smallALL$BT = factor(smallALL$BT) f1 <- pOverA(0.25, log2(100)) f2 <- function(x) (IQR(x) > 0.5) f3 <- ttest(smallALL$mol.biol, p=0.1) ff <- filterfun(f1, f2, f3) selectedsmallALL <- genefilter(exprs(smallALL), ff) smallALL = smallALL[selectedsmallALL, ] rm(f1) rm(f2) rm(f3) rm(ff) rm(bALL) sum(selectedsmallALL) set.seed(1357) population0<-InitialPopulation(smallALL, 14, 10, FALSE) individuals0<-Individuals(population0) results0<-EvaluationFunction(smallALL, individuals0, response="mol.biol", method=knn.cvI(k=3, l=2), trainTest="LOG") chrConf0<-splitChromosomes(smallALL) iterRes0<-AnalyzeResults(individuals0, results0, randomAssortment=TRUE, chrConf0) ## End(Not run)## Not run: library(genefilter) library(ALL) data(ALL) bALL = ALL[, substr(ALL$BT,1,1) == "B"] smallALL = bALL[, bALL$mol.biol %in% c("BCR/ABL", "NEG")] smallALL$mol.biol = factor(smallALL$mol.biol) smallALL$BT = factor(smallALL$BT) f1 <- pOverA(0.25, log2(100)) f2 <- function(x) (IQR(x) > 0.5) f3 <- ttest(smallALL$mol.biol, p=0.1) ff <- filterfun(f1, f2, f3) selectedsmallALL <- genefilter(exprs(smallALL), ff) smallALL = smallALL[selectedsmallALL, ] rm(f1) rm(f2) rm(f3) rm(ff) rm(bALL) sum(selectedsmallALL) set.seed(1357) population0<-InitialPopulation(smallALL, 14, 10, FALSE) individuals0<-Individuals(population0) results0<-EvaluationFunction(smallALL, individuals0, response="mol.biol", method=knn.cvI(k=3, l=2), trainTest="LOG") chrConf0<-splitChromosomes(smallALL) iterRes0<-AnalyzeResults(individuals0, results0, randomAssortment=TRUE, chrConf0) ## End(Not run)
Two-point crossover operator.
Crossover(c1, c2, chrConf)Crossover(c1, c2, chrConf)
c1 |
Set of chromosomes. |
c2 |
Set of chromosomes. |
chrConf |
Configuration of chromosomes returned by splitChromosomes(). |
## Not run: library(ALL) data(ALL) demoALL<-ALL[1:12,1:8] set.seed(1357) population02<-InitialPopulation(demoALL, 2, 4, FALSE) chrConf02<-splitChromosomes(demoALL, 2) chrConf02 population02[1:2,] Crossover(population02[1,], population02[2,], chrConf02) ## End(Not run)## Not run: library(ALL) data(ALL) demoALL<-ALL[1:12,1:8] set.seed(1357) population02<-InitialPopulation(demoALL, 2, 4, FALSE) chrConf02<-splitChromosomes(demoALL, 2) chrConf02 population02[1:2,] Crossover(population02[1,], population02[2,], chrConf02) ## End(Not run)
Initializes and starts the search with the genetic algorithm.
dGAselID(x, response, method = knn.cvI(k = 3, l = 2), trainTest = "LOG", startGenes, populationSize, iterations, noChr = 22, elitism = NA, ID = "ID1", pMutationChance = 0, nSMutationChance = 0, fSMutationChance = 0, lSDeletionChance = 0, wChrDeletionChance = 0, transposonChance = 0, randomAssortment = TRUE, embryonicSelection = NA, EveryGeneInInitialPopulation = TRUE, nnetSize = NA, nnetDecay = NA, rdaAlpha = NA, rdaDelta = NA, ...)dGAselID(x, response, method = knn.cvI(k = 3, l = 2), trainTest = "LOG", startGenes, populationSize, iterations, noChr = 22, elitism = NA, ID = "ID1", pMutationChance = 0, nSMutationChance = 0, fSMutationChance = 0, lSDeletionChance = 0, wChrDeletionChance = 0, transposonChance = 0, randomAssortment = TRUE, embryonicSelection = NA, EveryGeneInInitialPopulation = TRUE, nnetSize = NA, nnetDecay = NA, rdaAlpha = NA, rdaDelta = NA, ...)
x |
Dataset in ExpressionSet format. |
response |
Response variable |
method |
Supervised classifier for fitness evaluation. Most of the supervised classifiers in MLInterfaces are acceptable. The default is knn.cvI(k=3, l=2). |
trainTest |
Cross-validation method. The default is "LOG". |
startGenes |
Genes in the genotypes at initialization. |
populationSize |
Number of genotypes in initial population. |
iterations |
Number of iterations. |
noChr |
Number of chromosomes. The default value is 22. |
elitism |
Elite population in percentages. |
ID |
Dominance. The default value is "ID1". Use "ID2" for Incomplete Dominance. |
pMutationChance |
Chance for a Point Mutation to occur. The default value is 0. |
nSMutationChance |
Chance for a Non-sense Mutation to occur. The default value is 0. |
fSMutationChance |
Chance for a Frameshift Mutation to occur. The default value is 0. |
lSDeletionChance |
Chance for a Large Segment Deletion to occur. The default value is 0. |
wChrDeletionChance |
Chance for a Whole Chromosome Deletion to occur. The default value is 0. |
transposonChance |
Chance for a Transposon Mutation to occur. The default value is 0. |
randomAssortment |
Random Assortment of Chromosomes for recombinations. The default value is TRUE. |
embryonicSelection |
Remove chromosomes with fitness < specified value. The default value is NA. |
EveryGeneInInitialPopulation |
Request for every gene to be present in the initial population. The default value is TRUE. |
nnetSize |
for nnetI. The default value is NA. |
nnetDecay |
for nnetI. The default value is NA. |
rdaAlpha |
for rdaI. The default value is NA. |
rdaDelta |
for rdaI. The default value is NA. |
... |
Additional arguments. |
The output is a list containing 5 named vectors, records of the evolution:
DGenes |
The occurrences in selected genotypes for every gene, |
dGenes |
The occurrences in discarded genotypes for every gene, |
MaximumAccuracy |
Maximum accuracy in every generation, |
MeanAccuracy |
Average accuracy in every generation, |
MinAccuracy |
Minimum accuracy in every generation, |
BestIndividuals |
Best individual in every generation. |
## Not run: library(genefilter) library(ALL) data(ALL) bALL = ALL[, substr(ALL$BT,1,1) == "B"] smallALL = bALL[, bALL$mol.biol %in% c("BCR/ABL", "NEG")] smallALL$mol.biol = factor(smallALL$mol.biol) smallALL$BT = factor(smallALL$BT) f1 <- pOverA(0.25, log2(100)) f2 <- function(x) (IQR(x) > 0.5) f3 <- ttest(smallALL$mol.biol, p=0.1) ff <- filterfun(f1, f2, f3) selectedsmallALL <- genefilter(exprs(smallALL), ff) smallALL = smallALL[selectedsmallALL, ] rm(f1) rm(f2) rm(f3) rm(ff) rm(bALL) sum(selectedsmallALL) set.seed(149) res<-dGAselID(smallALL, "mol.biol", trainTest=1:79, startGenes=12, populationSize=200, iterations=150, noChr=5, pMutationChance=0.0075, elitism=4) ## End(Not run)## Not run: library(genefilter) library(ALL) data(ALL) bALL = ALL[, substr(ALL$BT,1,1) == "B"] smallALL = bALL[, bALL$mol.biol %in% c("BCR/ABL", "NEG")] smallALL$mol.biol = factor(smallALL$mol.biol) smallALL$BT = factor(smallALL$BT) f1 <- pOverA(0.25, log2(100)) f2 <- function(x) (IQR(x) > 0.5) f3 <- ttest(smallALL$mol.biol, p=0.1) ff <- filterfun(f1, f2, f3) selectedsmallALL <- genefilter(exprs(smallALL), ff) smallALL = smallALL[selectedsmallALL, ] rm(f1) rm(f2) rm(f3) rm(ff) rm(bALL) sum(selectedsmallALL) set.seed(149) res<-dGAselID(smallALL, "mol.biol", trainTest=1:79, startGenes=12, populationSize=200, iterations=150, noChr=5, pMutationChance=0.0075, elitism=4) ## End(Not run)
Operator for elitism.
Elitism(results, elitism, ID)Elitism(results, elitism, ID)
results |
Results returned by EvaluationFunction(). |
elitism |
Elite population in percentages. |
ID |
Dominance. The default value is "ID1". Use "ID2" for Incomplete Dominance. |
## Not run: library(genefilter) library(ALL) data(ALL) bALL = ALL[, substr(ALL$BT,1,1) == "B"] smallALL = bALL[, bALL$mol.biol %in% c("BCR/ABL", "NEG")] smallALL$mol.biol = factor(smallALL$mol.biol) smallALL$BT = factor(smallALL$BT) f1 <- pOverA(0.25, log2(100)) f2 <- function(x) (IQR(x) > 0.5) f3 <- ttest(smallALL$mol.biol, p=0.1) ff <- filterfun(f1, f2, f3) selectedsmallALL <- genefilter(exprs(smallALL), ff) smallALL = smallALL[selectedsmallALL, ] rm(f1) rm(f2) rm(f3) rm(ff) rm(bALL) sum(selectedsmallALL) set.seed(1357) population0<-InitialPopulation(smallALL, 14, 8, FALSE) individuals0<-Individuals(population0) results0<-EvaluationFunction(smallALL, individuals0, response="mol.biol", method=knn.cvI(k=3, l=2), trainTest="LOG") Elitism(results0, 25, ID="ID1") Elitism(results0, 25, ID="ID2") ## End(Not run)## Not run: library(genefilter) library(ALL) data(ALL) bALL = ALL[, substr(ALL$BT,1,1) == "B"] smallALL = bALL[, bALL$mol.biol %in% c("BCR/ABL", "NEG")] smallALL$mol.biol = factor(smallALL$mol.biol) smallALL$BT = factor(smallALL$BT) f1 <- pOverA(0.25, log2(100)) f2 <- function(x) (IQR(x) > 0.5) f3 <- ttest(smallALL$mol.biol, p=0.1) ff <- filterfun(f1, f2, f3) selectedsmallALL <- genefilter(exprs(smallALL), ff) smallALL = smallALL[selectedsmallALL, ] rm(f1) rm(f2) rm(f3) rm(ff) rm(bALL) sum(selectedsmallALL) set.seed(1357) population0<-InitialPopulation(smallALL, 14, 8, FALSE) individuals0<-Individuals(population0) results0<-EvaluationFunction(smallALL, individuals0, response="mol.biol", method=knn.cvI(k=3, l=2), trainTest="LOG") Elitism(results0, 25, ID="ID1") Elitism(results0, 25, ID="ID2") ## End(Not run)
Function for deleting individuals with a fitness below a specified threshold.
EmbryonicSelection(population, results, embryonicSelection)EmbryonicSelection(population, results, embryonicSelection)
population |
Population of individuals with diploid genotypes. |
results |
Results returned by EvaluationFunction(). |
embryonicSelection |
Threshold value. The default value is NA. |
## Not run: library(genefilter) library(ALL) data(ALL) bALL = ALL[, substr(ALL$BT,1,1) == "B"] smallALL = bALL[, bALL$mol.biol %in% c("BCR/ABL", "NEG")] smallALL$mol.biol = factor(smallALL$mol.biol) smallALL$BT = factor(smallALL$BT) f1 <- pOverA(0.25, log2(100)) f2 <- function(x) (IQR(x) > 0.5) f3 <- ttest(smallALL$mol.biol, p=0.1) ff <- filterfun(f1, f2, f3) selectedsmallALL <- genefilter(exprs(smallALL), ff) smallALL = smallALL[selectedsmallALL, ] rm(f1) rm(f2) rm(f3) rm(ff) rm(bALL) sum(selectedsmallALL) set.seed(1357) population0<-InitialPopulation(smallALL, 14, 8, FALSE) individuals0<-Individuals(population0) results0<-EvaluationFunction(smallALL, individuals0, response="mol.biol", method=knn.cvI(k=3, l=2), trainTest="LOG") EmbryonicSelection(individuals0, results0, 0.5) ## End(Not run)## Not run: library(genefilter) library(ALL) data(ALL) bALL = ALL[, substr(ALL$BT,1,1) == "B"] smallALL = bALL[, bALL$mol.biol %in% c("BCR/ABL", "NEG")] smallALL$mol.biol = factor(smallALL$mol.biol) smallALL$BT = factor(smallALL$BT) f1 <- pOverA(0.25, log2(100)) f2 <- function(x) (IQR(x) > 0.5) f3 <- ttest(smallALL$mol.biol, p=0.1) ff <- filterfun(f1, f2, f3) selectedsmallALL <- genefilter(exprs(smallALL), ff) smallALL = smallALL[selectedsmallALL, ] rm(f1) rm(f2) rm(f3) rm(ff) rm(bALL) sum(selectedsmallALL) set.seed(1357) population0<-InitialPopulation(smallALL, 14, 8, FALSE) individuals0<-Individuals(population0) results0<-EvaluationFunction(smallALL, individuals0, response="mol.biol", method=knn.cvI(k=3, l=2), trainTest="LOG") EmbryonicSelection(individuals0, results0, 0.5) ## End(Not run)
Evaluates the individuals' fitnesses.
EvaluationFunction(x, individuals, response, method, trainTest, nnetSize = NA, nnetDecay = NA, rdaAlpha = NA, rdaDelta = NA, ...)EvaluationFunction(x, individuals, response, method, trainTest, nnetSize = NA, nnetDecay = NA, rdaAlpha = NA, rdaDelta = NA, ...)
x |
Dataset in ExpressionSet format. |
individuals |
Population of individuals with diploid genotypes. |
response |
Response variable. |
method |
Supervised classifier for fitness evaluation. Most of the supervised classifiers in MLInterfaces are acceptable. The default is knn.cvI(k=3, l=2). |
trainTest |
Cross-validation method. The default is "LOG". |
nnetSize |
for nnetI. The default value is NA. |
nnetDecay |
for nnetI. The default value is NA. |
rdaAlpha |
for rdaI. The default value is NA. |
rdaDelta |
for rdaI. The default value is NA. |
... |
Additional arguments. |
## Not run: library(genefilter) library(ALL) data(ALL) bALL = ALL[, substr(ALL$BT,1,1) == "B"] smallALL = bALL[, bALL$mol.biol %in% c("BCR/ABL", "NEG")] smallALL$mol.biol = factor(smallALL$mol.biol) smallALL$BT = factor(smallALL$BT) f1 <- pOverA(0.25, log2(100)) f2 <- function(x) (IQR(x) > 0.5) f3 <- ttest(smallALL$mol.biol, p=0.1) ff <- filterfun(f1, f2, f3) selectedsmallALL <- genefilter(exprs(smallALL), ff) smallALL = smallALL[selectedsmallALL, ] rm(f1) rm(f2) rm(f3) rm(ff) rm(bALL) sum(selectedsmallALL) set.seed(1357) population0<-InitialPopulation(smallALL, 14, 8, FALSE) individuals0<-Individuals(population0) results<-EvaluationFunction(smallALL, individuals0, response="mol.biol", method=knn.cvI(k=3, l=2), trainTest="LOG") ## End(Not run)## Not run: library(genefilter) library(ALL) data(ALL) bALL = ALL[, substr(ALL$BT,1,1) == "B"] smallALL = bALL[, bALL$mol.biol %in% c("BCR/ABL", "NEG")] smallALL$mol.biol = factor(smallALL$mol.biol) smallALL$BT = factor(smallALL$BT) f1 <- pOverA(0.25, log2(100)) f2 <- function(x) (IQR(x) > 0.5) f3 <- ttest(smallALL$mol.biol, p=0.1) ff <- filterfun(f1, f2, f3) selectedsmallALL <- genefilter(exprs(smallALL), ff) smallALL = smallALL[selectedsmallALL, ] rm(f1) rm(f2) rm(f3) rm(ff) rm(bALL) sum(selectedsmallALL) set.seed(1357) population0<-InitialPopulation(smallALL, 14, 8, FALSE) individuals0<-Individuals(population0) results<-EvaluationFunction(smallALL, individuals0, response="mol.biol", method=knn.cvI(k=3, l=2), trainTest="LOG") ## End(Not run)
Operator for the frameshift mutation.
frameShiftMutation(individuals, chrConf, mutationChance)frameShiftMutation(individuals, chrConf, mutationChance)
individuals |
dataset returned by Individuals(). |
chrConf |
Configuration of chromosomes returned by splitChromosomes(). |
mutationChance |
Chance for a frameshift mutation to occur. |
## Not run: library(ALL) data(ALL) demoALL<-ALL[1:12,1:8] set.seed(1234) population<-InitialPopulation(demoALL, 4, 9) individuals<-Individuals(population) chrConf<-splitChromosomes(demoALL, 2) chrConf individuals set.seed(123) frameShiftMutation(individuals, chrConf, 20) ## End(Not run)## Not run: library(ALL) data(ALL) demoALL<-ALL[1:12,1:8] set.seed(1234) population<-InitialPopulation(demoALL, 4, 9) individuals<-Individuals(population) chrConf<-splitChromosomes(demoALL, 2) chrConf individuals set.seed(123) frameShiftMutation(individuals, chrConf, 20) ## End(Not run)
Generates individuals with diploid genotypes.
Individuals(population)Individuals(population)
population |
Population of haploid genotypes. |
## Not run: library(ALL) data(ALL) demoALL<-ALL[1:12,1:8] population02<-InitialPopulation(demoALL, 20, 4, FALSE) individuals02<-Individuals(population02) ## End(Not run)## Not run: library(ALL) data(ALL) demoALL<-ALL[1:12,1:8] population02<-InitialPopulation(demoALL, 20, 4, FALSE) individuals02<-Individuals(population02) ## End(Not run)
Generates an initial randomly generated population of haploid genotypes.
InitialPopulation(x, populationSize, startGenes, EveryGeneInInitialPopulation = TRUE)InitialPopulation(x, populationSize, startGenes, EveryGeneInInitialPopulation = TRUE)
x |
Dataset in ExpressionSet format. |
populationSize |
Number of genotypes in initial population. |
startGenes |
Genes in the genotypes at initialization. |
EveryGeneInInitialPopulation |
Request for every gene to be present in the initial population. The default value is TRUE. |
## Not run: library(ALL) data(ALL) demoALL<-ALL[1:12,1:8] population01<-InitialPopulation(demoALL, 4, 4) population02<-InitialPopulation(demoALL, 20, 4, FALSE) ## End(Not run)## Not run: library(ALL) data(ALL) demoALL<-ALL[1:12,1:8] population01<-InitialPopulation(demoALL, 4, 4) population02<-InitialPopulation(demoALL, 20, 4, FALSE) ## End(Not run)
Operator for the large segment deletion.
largeSegmentDeletion(individuals, chrConf, mutationChance)largeSegmentDeletion(individuals, chrConf, mutationChance)
individuals |
dataset returned by Individuals(). |
chrConf |
Configuration of chromosomes returned by splitChromosomes(). |
mutationChance |
Chance for a large segment deletion mutation to occur. |
## Not run: library(ALL) data(ALL) demoALL<-ALL[1:12,1:8] set.seed(1234) population<-InitialPopulation(demoALL, 4, 9) individuals<-Individuals(population) chrConf<-splitChromosomes(demoALL, 2) chrConf individuals set.seed(123) largeSegmentDeletion(individuals, chrConf, 20) ## End(Not run)## Not run: library(ALL) data(ALL) demoALL<-ALL[1:12,1:8] set.seed(1234) population<-InitialPopulation(demoALL, 4, 9) individuals<-Individuals(population) chrConf<-splitChromosomes(demoALL, 2) chrConf individuals set.seed(123) largeSegmentDeletion(individuals, chrConf, 20) ## End(Not run)
Operator for the nonsense mutation.
nonSenseMutation(individuals, chrConf, mutationChance)nonSenseMutation(individuals, chrConf, mutationChance)
individuals |
dataset returned by Individuals(). |
chrConf |
Configuration of chromosomes returned by splitChromosomes(). |
mutationChance |
Chance for a nonsense mutation to occur. |
## Not run: library(ALL) data(ALL) demoALL<-ALL[1:12,1:8] set.seed(1234) population<-InitialPopulation(demoALL, 4, 9) individuals<-Individuals(population) chrConf<-splitChromosomes(demoALL, 2) chrConf individuals set.seed(123) nonSenseMutation(individuals, chrConf, 20) ## End(Not run)## Not run: library(ALL) data(ALL) demoALL<-ALL[1:12,1:8] set.seed(1234) population<-InitialPopulation(demoALL, 4, 9) individuals<-Individuals(population) chrConf<-splitChromosomes(demoALL, 2) chrConf individuals set.seed(123) nonSenseMutation(individuals, chrConf, 20) ## End(Not run)
Function for graphically representing the evolution.
PlotGenAlg(DGenes, dGenes, maxEval, meanEval)PlotGenAlg(DGenes, dGenes, maxEval, meanEval)
DGenes |
Occurences of genes as dominant. |
dGenes |
Occurences of genes as recessive. For future developments. |
maxEval |
Maximum fitness. |
meanEval |
Average fitness. |
## Not run: #Graphical representation of the evolution after each generation. #Intended to be used by dGAselID() only. #Please refer to the example for dGAselID(). ## End(Not run)## Not run: #Graphical representation of the evolution after each generation. #Intended to be used by dGAselID() only. #Please refer to the example for dGAselID(). ## End(Not run)
Operator for the point mutation.
pointMutation(individuals, mutationChance)pointMutation(individuals, mutationChance)
individuals |
dataset returned by Individuals(). |
mutationChance |
chance for a point mutation to occur. |
## Not run: library(ALL) data(ALL) demoALL<-ALL[1:12,1:8] set.seed(1234) population<-InitialPopulation(demoALL, 4, 9) individuals<-Individuals(population) individuals set.seed(123) pointMutation(individuals, 4) ## End(Not run)## Not run: library(ALL) data(ALL) demoALL<-ALL[1:12,1:8] set.seed(1234) population<-InitialPopulation(demoALL, 4, 9) individuals<-Individuals(population) individuals set.seed(123) pointMutation(individuals, 4) ## End(Not run)
Random assortment of chromosomes operator.
RandomAssortment(newChrs, chrConf)RandomAssortment(newChrs, chrConf)
newChrs |
Set of chromosomes. |
chrConf |
Configuration of chromosomes returned by splitChromosomes(). |
## Not run: library(ALL) data(ALL) demoALL<-ALL[1:12,1:8] population02<-InitialPopulation(demoALL, 2, 4, FALSE) chrConf02<-splitChromosomes(demoALL, 4) set.seed(1357) cr1<-Crossover(population02[1,], population02[2,], chrConf02) RandomAssortment(cr1, chrConf02) cr1 chrConf02 ## End(Not run)## Not run: library(ALL) data(ALL) demoALL<-ALL[1:12,1:8] population02<-InitialPopulation(demoALL, 2, 4, FALSE) chrConf02<-splitChromosomes(demoALL, 4) set.seed(1357) cr1<-Crossover(population02[1,], population02[2,], chrConf02) RandomAssortment(cr1, chrConf02) cr1 chrConf02 ## End(Not run)
Generates a random population for the next generation.
RandomizePop(population)RandomizePop(population)
population |
Population of chromosome sets in current generation. |
## Not run: library(ALL) data(ALL) demoALL<-ALL[1:12,1:8] population01<-InitialPopulation(demoALL, 4, 4) population01 RandomizePop(population01) ## End(Not run)## Not run: library(ALL) data(ALL) demoALL<-ALL[1:12,1:8] population01<-InitialPopulation(demoALL, 4, 4) population01 RandomizePop(population01) ## End(Not run)
Divides the genotypes into sets with a desired number of chromosomes.
splitChromosomes(x, noChr = 22)splitChromosomes(x, noChr = 22)
x |
Dataset in ExpressionSet format. |
noChr |
Desired number of chromosomes. The default value is 22. |
## Not run: library(ALL) data(ALL) demoALL<-ALL[1:12,1:8] splitChromosomes(demoALL, 3) splitChromosomes(demoALL) ## End(Not run)## Not run: library(ALL) data(ALL) demoALL<-ALL[1:12,1:8] splitChromosomes(demoALL, 3) splitChromosomes(demoALL) ## End(Not run)
Operator for transposons.
transposon(individuals, chrConf, mutationChance)transposon(individuals, chrConf, mutationChance)
individuals |
dataset returned by Individuals(). |
chrConf |
Configuration of chromosomes returned by splitChromosomes(). |
mutationChance |
Chance for a transposon mutation to occur. |
## Not run: library(ALL) data(ALL) demoALL<-ALL[1:12,1:8] set.seed(1234) population<-InitialPopulation(demoALL, 4, 9) individuals<-Individuals(population) chrConf<-splitChromosomes(demoALL, 2) chrConf individuals set.seed(123) transposon(individuals, chrConf, 20) ## End(Not run)## Not run: library(ALL) data(ALL) demoALL<-ALL[1:12,1:8] set.seed(1234) population<-InitialPopulation(demoALL, 4, 9) individuals<-Individuals(population) chrConf<-splitChromosomes(demoALL, 2) chrConf individuals set.seed(123) transposon(individuals, chrConf, 20) ## End(Not run)
Operator for the deletion of a whole chromosome.
wholeChromosomeDeletion(individuals, chrConf, mutationChance)wholeChromosomeDeletion(individuals, chrConf, mutationChance)
individuals |
dataset returned by Individuals(). |
chrConf |
Configuration of chromosomes returned by splitChromosomes(). |
mutationChance |
Chance for a deletion of a whole chromosome mutation to occur. |
## Not run: library(ALL) data(ALL) demoALL<-ALL[1:12,1:8] set.seed(1234) population<-InitialPopulation(demoALL, 4, 9) individuals<-Individuals(population) chrConf<-splitChromosomes(demoALL, 2) chrConf individuals set.seed(123) wholeChromosomeDeletion(individuals, chrConf, 20) ## End(Not run)## Not run: library(ALL) data(ALL) demoALL<-ALL[1:12,1:8] set.seed(1234) population<-InitialPopulation(demoALL, 4, 9) individuals<-Individuals(population) chrConf<-splitChromosomes(demoALL, 2) chrConf individuals set.seed(123) wholeChromosomeDeletion(individuals, chrConf, 20) ## End(Not run)