Algorithm in Experimenter

[willie1]

[ FlexSim 23.0.0 ]

Hello Guys, I'm currently trying to implement functionality similar to the algorithm in Experimenter, and I have two questions.

1. Since in Experimenter, the number of replications for each scenario is user-defined and fixed, is it possible to allocate the number of replications based on the performance results? For example, if the performance results for the first stage of scenarios are ranked as 1>3>5>4>2, can I allocate replications accordingly, such as running 5 replications for scenario 1, 4 replications for scenario 3, and so on in a loop? Is this method currently achievable?
2. If not ,can Experimenter interact with external Python scripts or the other way to implement, because I have seen other related comments on the forum, and it seems that OptQuset is not supported.?

Thanks for your help and advise

[joerg_vogel_HsH]

There is significant error in your approach. Executing of experiments is not linear. It is more a chaotic execution of scenarios and replications. You can run an experiment (all scenarios and replications), then you can evaluate the results and change parameters.

Knowing this I would run experiments with small variants more often to distinguish whether the change of parameter values brings better results.

[willie1]

I apologize for the error in my previous description. The performance results for the first stage of scenarios are ranked as 1>3>5>4>2. What I want to do is to replicate the best scenario(1) 5 times, the second best scenario (3) 4 times, and not replicate scenario (2) 4 times, which might cause a misunderstanding.

So, it's not about distributing replication counts linearly but based on the performance ranking of the previous stage.

[willie1]

But I understand what you mean. It seems that my approach cannot be directly implemented in Experimenter. I will try other methods and thank you for your advice.

[jason_lightfoot_adsk]

Hi @Willie Chung,

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