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The GenEstim function presented here uses a very simple genetic algorithm to estimate parameters. The function returns the best estimated set of parameters (\$estim), the AIC (\$information) at each generation, and the cost of the best model (\$bestcost) at each generation.

Results of running the program with a logistic function : ```Logis = function(x,p)	p[]/(1+p[]*exp(-p[]*x))
{
k <- length(par)
n <- length(yvalues)
return(aic)
}
P	<-	list(2,10,4)
X	<-	seq(from=-5,to=5,by=0.1)
Y	<-	Logis(X,P) + rnorm(length(X),sd=0.1)
plot(X,Y,pch=19,col='grey')
GenEstim	<- function(
start.pars,
...,
numiter = 1e3,
npop = 1e2)
{
bestcost <- NULL
cur.AIC <- NULL
for(it in 1:numiter)
{
pop <- matrix(0,ncol=length(start.pars),nrow=npop)
for(p in 1:length(start.pars))
{
pop[,p] 	<- rnorm(npop,start.pars[[p]],sd=1)
pop[1,p]	<- start.pars[[p]]
}
Costs <- NULL
for(i in 1:nrow(pop))
{
li <- as.list(pop[i,])
Costs[i] <- cost(li)
}
bestcost <- c(bestcost,min(Costs))
best <-which.min(Costs)
start.pars <- as.list(pop[best,])
}
return(list(estim=start.pars,information=cur.AIC,convergence=bestcost))
}

simul <- GenEstim(list(0,0,0))
x.control <- seq(from=-6,to=6,by=0.1)
lines(x.control,Logis(x.control,simul\$estim),col='orange',lwd=3)```  