similar to: Minimizing two non-linear functions with genoud - Trying to minimize or converge near zero

Hi R users, "genoud" function of "rgenoud" package will optimize my function. If opt = genoud(fn,2,max=TRUE,starting.value=c(1,10),........) opt$value will give the optimized value of the function, "fn". My problem is from the same opt, can I get the value of the function at the initial parameter values? I need the initial value of the function for

I would like to limit the significant figures of the calibrated parameters determined by genoud() in the Rgenoud package. Below is some example output, where column 1 is model run number, columns 2-7 are the parameter values, and columns 8-12 are model fit statistics. I would like genoud to internally limit parameters to 4 decimal places as shown in this output. It is clear that the function is

Hello - and sorry for a possibly stupid question, I'm just starting to learn rgenoud. I am defining a function with 5 parameters (p1, p2, p3, p4a, and p4b) and then want to optimize it using genoud. But I am doing something wrong. Before genoud is even able to run it says: "Error in p2 + 1.2 : 'p2' is missing". I assume I did not specify it right. My code is below. The task

Greetings R users, maybe there is someone who can help me with this problem: I'm trying to fit this discontinous model : GE<-data.frame( Ci<-c(81,87,91,111,159,173,295,453,629,984), A<-c(-0.9,1.2,3.5,8.3,13.1,14.4,22.9,27.3,29.6,32.6) ) rhs <- function(Ci, Vcmax, J, Rd) { ? ?R <-0.008314472 ? ?Tleaf <-25 ? ?Kc <-exp(38.05-79.43/(R*(Tleaf+273.15))) ? ?Ko

Hello, I am trying to optimise a nonlinear model to derive 'best-fit' parameter esimates using the genoud function. I have been using the genetic algorithm - gafit - in order to do this, but I am getting parameter estimates that do not always reach the global minimum. I am very keen to apply genoud to optimising this model to see if my results will improve, and also out of personal

Dear Friends, I have been trying to learn how to use the derivative free optimization algorithms implemented in the package RGENOUD by Mebane and Sekhon. However, it does not seem to work for reasons best described as my total ignorance. If anybody has experience using this package, it would be really helpful if you can point out where I'm making a mistake. Thanks in advance Anup Sample

Hi; I have a R script that includes a call to genoud(); genoud process lasts about 4 seconds, what would be OK if I hadn't have to call it about 2000 times. This yields about 2 hours of processing. And I would like to use this script operationally; so that it should be run twice a day. It seems to me that the parallel processing option included in genoud() divides the task inside the function

Dear All I am using rgenoud to solve the following maximization problem: myfunc <- function(x) { x1 <- x[1] x2 <- x[2] if (x1^2+x2^2 > 1) return(-9999999) else x1+x2 } genoud(myfunc, nvars=2, Domains=rbind(c(0,1),c(0,1)),max=TRUE,boundary.enforcement=2,solution.tolerance=0.000001) How can one increase the precision of the solution $par [1] 0.7072442 0.7069694 ? I

Dear R user fellows, I would like to ask you about the package 'rgenoud' which is a genetic optimization tool. I ran the function 'genoud' with two variables to be minimized by the following command. result<-genoud(fn,nvars=2,starting.values=c(0.5,0), pop.size=1000, max.generations=10, wait.generations=3) Then, I had the following error message. Error in

Hello! I have read somewhere (somehow, I can't seem to find it again, it's been a couple of months) that when analyzing factorial block design, the position where you put the block factor is important, even more when there are missing values. I understand that when using anova.lm, the order is sequential, so that if I want to check for a treatment effect, I should put my blocking factor

I am trying to use the optimizing function genoud() with the snow package on a couple of i686 machines running Redhat Linux WS4 . I don't know anything about PVM or MPI, so I just followed the directions in snow and rgenoud for the simplest method and started a socket cluster. My function fn for genoud involves updating an input file for a separate numerical model with the latest parameter

Dear All,   I could use a bit of help here, this function is hard to figure out (for me at least) I have the following so far:   PKindex<-data.frame(Subject=c(1),time=c(1,2,3,4,6,10,12),conc=c(32,28,25,22,18,14,11)) Dose<-200 Tinf <-0.5   defun<- function(time, y, parms) {  dCpdt <- -parms["kel"] * y[1]  list(dCpdt)  } modfun <- function(time,kel, Vd) {   out <-

Hello, After getting help to solve part of my problem and some delay on my part, I am posting a more refined version to see if someone can help me further. I am trying to autocalibrate a model in my subject area using the snow and rgenoud packages. I want to use the key function "fn" that is called by genoud() to finalize input, run the model executable, and compute the objective

I have been using the rgenoud package for a nonlinear least-squares problem with lots of local minima, and it works very well but takes lots of time. According to the article refrenced in the documentation, the original GENOUD-software by the same authors seems to allow for user-supplied analytical derivatives instead of numerical approximations, which would probably save some time. Does anybody

I've been using RGenoud for a while and it worked smoothly so far. However I came across a strange problem lately (for me at least...). It fails after the first individual and I get the following error message when I set MemoryMatrix = FALSE : Error in genoud(fn, nvars = 8, max = TRUE, pop.size = 10, : REAL() can only be applied to a 'numeric', not a 'character' when I

I have a model with 5 parameters that I am optimising where the (best) value of the objective function is negative. I would like to use the Hessian matrix (from genoud and/or optim functions) to construct the covariance and correlation matrices. This is the code that I am using: est <- out$par # Parameter estimates H <- out$hessian # Hessian V <-

hi people, I'm trying to maximize this function: fn= function (x) {x[1]^2+5*x[2]^2} with this restriction fn1 = function (x) {x[1]+x[2] <=5} Can someone help me how to procedure this? I tried in the alabama and genoud package but i have problems with the setting of constrains. Regards, Eliano -- View this message in context:

Hi, I am looking for genetic optimization package that allow to define my own chromosome solution and crossover/mutation opoerations. I checked genoud and genopt, not with much effort of 'cause, with no luck. Any one can shed some light on this? thanks.

Dear all, I would like to solve the following problem, which can be done with optimal control theory or dynamic programming: max(x,y) a*u1+b*u2+c*f1(u2) s.t. 0<u1<x, 0<u2<f2(x,u2), x'=f3(u1,u2,x) which can be rewritten if optimal control theory should be applied as H=a*u1+b*u2+c*f1(u2)+lambda*(x') s.t. 0<u1<x, 0<u2<f2(x,u2) The maximum principle

I wondered was people on this list felt about this article: http://www.voxeu.org/index.php?q=node/2363 which talks about the problems of obtaining sound answers in numerical optimisation in settings such as MLE or NLS. -- Ajay Shah http://www.mayin.org/ajayshah ajayshah at mayin.org http://ajayshahblog.blogspot.com <*(:-? -