xegaGaReplicateGene {xegaGaGene} | R Documentation |
Replicates a gene.
Description
xegaGaReplicateGene()
replicates a gene
by applying a gene reproduction pipeline
which uses crossover and
mutation and finishes with an acceptance rule.
The control flow starts
by selecting a gene from the population
followed by the case distinction:
-
Check if the mutation operation should be applied. (
mut
isTRUE
with a probability oflF$MutationRate()
). -
Check if the crossover operation should be applied. (
cross
isTRUE
with a probability oflF$CrossRate()
).
The state distinction determines which genetic operations are performed.
Usage
xegaGaReplicateGene(pop, fit, lF)
Arguments
pop |
Population of binary genes. |
fit |
Fitness vector. |
lF |
Local configuration of the genetic algorithm. |
Details
xegaGaReplicateGene()
implements the control flow
by a dynamic definition of the operator pipeline depending
on the random choices for mutation and crossover:
A gene
g
is selected and the boolean variablesmut
andcross
are set torunif(1)<rate
.The local function for the operator pipeline
OPpip(g, lF)
is defined by the truth values ofcross
andmut
:-
(cross==FALSE) & (mut==FALSE)
: Identity function. -
(cross==TRUE) & (mut==TRUE)
: Mate selection, crossover, mutation. -
(cross==TRUE) & (mut==FALSE)
: Mate selection, crossover. -
(cross==FALSE) & (mut==TRUE)
: Mutation.
-
Perform the operator pipeline and accept the result. The acceptance step allows the combination of a genetic algorithm with other heuristic algorithms like simulated annealing by executing an acceptance rule. For the genetic algorithm, the identity function is used.
Value
A list of one gene.
See Also
Other Replication:
xegaGaReplicate2Gene()
Examples
lFxegaGaGene$CrossGene<-xegaGaCrossGene
lFxegaGaGene$MutationRate<-function(fit, lF) {0.001}
lFxegaGaGene$Accept<-function(OperatorPipeline, gene, lF) {gene}
pop10<-lapply(rep(0,10), function(x) xegaGaInitGene(lFxegaGaGene))
epop10<-lapply(pop10, lFxegaGaGene$EvalGene, lF=lFxegaGaGene)
fit10<-unlist(lapply(epop10, function(x) {x$fit}))
newgenes<-xegaGaReplicateGene(pop10, fit10, lFxegaGaGene)