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Closed Feature request MC-1712

New Optimization Mode: Random Sampling

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icon_info.png This issue has been closed with status "Not Reviewed" and resolution "Not determined".

I have been using genetic optimization quite a lot to find the best parameters for my system and it works great. My understanding is that by design it outputs a subset that represents the "best" parameter values of all possible combinations, and this is obviously quite useful.
What I'd like to also be able to do is have it produce a random sampling of all possible combinations (i.e. not just the "best") so that I can then evaluate the risk of selecting the "wrong" parameters. For example, in most cases the full optimization is just too big to even be run in a reasonable amount of time much less be further analyzed in a spreadsheet.
This I think should be a conceptually simple modification of the Exhasutive mode whereas instead of running and outputting all possible combinations, the optimizer would randomly sample from all possible combinations (no replacement) and output just the randomly selected ones. The user should be able to control the sample size via an input for the desired % of total population (which should then be kept as the default value for future optimizations until changed).
Please let me know if this might be something that could be added, and feel free to contact me should you have any questions or if you would like to discuss further.

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