similar to: setting up a genoud run

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

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

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

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'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

Hello R users, I am trying to minimize two functions with genoud. It is actually one function with two sets of data, each of them having two unknown variables (called Vcmax and gi) which have the same value in each of the function. They are called f.1 and f.2 in the code below. My objective to minimize the functions in order to get the two variables equal in each of the functions. Furthermore, I

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 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

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

Dear everyone, I am trying to run rgenoud on several chips simultaneusly. I used the instructions provided on Jasjeet Sekhon's Homepage (http://sekhon.berkeley.edu/rgenoud/multiple_cpus.html). However, I have the newer version of R (R 2.12) installed - for a 64-bit machine. So, when I tried to install the special version of "snow" from a zip file provided by Jasjeet on his page, R

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

I am trying to use rgenoud and snow with an external model and file I/O. Unlike the typical application of rgenoud and snow, I need to run an external executable and do pre- and post-processing of input and output files for each parameter set generated by genoud(). I'm hoping that someone can suggest improvements to my approach and a work-around for file I/O problems I've encountered when

Hello! I have 2 variables - predictor "pred" and response variable "DV": pred<-c(439635.053, 222925.718, 668434.755, 194242.330, 5786.321, 115537.344, 100835.368, 7133.206, 159058.286, 4079991.629, 3380078.060, 2661279.136, 2698324.478, 1245213.965, 1901815.503, 1517019.451, 1396857.736, 1034030.988, 432249.574, 342329.325, 1831335.792, 2209578.859, 1641709.754,

Dear R users, Being a R-novice using version 1.7.0 I have not managed to solve the following problem, so I turn to you: I have 12 pairs nonlinear curves having some parameters (p1,p2,p3) in common and others specific to one of the curves (p4a,p4b). I want to fit a nonlinear mixed model with nmle with the random effects varying over the 12 samples (pairs). In order to be able to estimate the

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 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

Dear R group, since I do only have a moderately fast MacBook and I would like to get my results faster than within 72h per run ( :-((( ), I tried Amazon AWS, which offers pretty fast computers via remote access. I don't want to post any code because its several hundreds lines of code, I am not looking for the "optimal" answer, but maybe some suggestions from you if faced similar

Hi, i found out that Syslinux has a problem with Asus P4B Board. Even if I use the -s compability option i get always 'Boot failed'. Do you plan to fix this? If you need further information or a tester for this problem please contact me. Greetings Markus Tavenrath

This is my code i don't understand the error message: library(rgenoud) rm(list=ls()) set.seed(666) ######################################################### # As a first step, it is assumed that all input parameters are independent of ageing : ######################################################### InputDim <-20 # Max number of ageings in the inputs CPIRate <- rep(0.02 , InputDim ) #