Monday, March 20, 2023

Annoying Message with R GA Package

This is a small technical note (mainly a reminder to myself) about using the GA package for genetic algorithms in R, specifically within an R Markdown document (such as an R notebook). The GA::ga() function includes an optional argument to turn on parallel evaluation of the fitness function. The argument value can be true or false (default), which are self-explanatory, or a number of cores, or a character string ("snow" or "multicore") for how parallelization is to be done. The default is "multicore" except on Windows, where "snow" is apparently the only option. When I choose to turn on parallel objective evaluation, I take the easy route and just set it to TRUE.

When run in a terminal, I just get a sequence of log entries, one per generation, and then it terminates. That's also what I see when the command is run in an R Markdown document ... until the document is rendered to HTML. In the HTML output, interspersed with each line of log output is a block of four identical copies of the following.

loaded GA and set parent environment

The fact that there are four copies is presumably because my PC has four cores. Turning off the monitor option gets rid of the progress printouts but does not eliminate the repetitive messages. Switching the parallel mode to "snow" does get rid of them, but I'm not sure what the broader implications of that are.

After significant spelunking, I traced the messages to the doParallel R package, which is apparently being used to implement parallel processing under the "multicore" option. In any event, the way to get rid of them is to add the option "message = FALSE" to the R chunk in which ga() is being executed. Barring other chunk options, this would change {r} to {r, message = FALSE} for that one chunk. (You can also set it as a global option in the document.) Happily, the monitor output (stats for each generation) still prints out. It just gets rid of those <expletive deleted> parallel processing messages.

No comments:

Post a Comment

Due to intermittent spamming, comments are being moderated. If this is your first time commenting on the blog, please read the Ground Rules for Comments. In particular, if you want to ask an operations research-related question not relevant to this post, consider asking it on Operations Research Stack Exchange.