Optimization Hill Climbing Random Seed Logic

Dear All,
can someone tell me what rule is behind the random seed in Knime?
Im about to find the global maximum of an objective function. Since I am aware of the problems of the Hill Climbing, i have used the Brute Force as a reference value. I would like to get to the maximum with the Hill Climbing Algorithm using different starting points.
Can anyone help?
I would be very grateful about example workflows, at best an explanation of the Random Seed logic.

Thanks a lot!

Dear @JayJay92,

You can find a generic example here:
https://hub.knime.com/knime/workflows/Examples/04_Analytics/11_Optimization/06_Parameter_Optimization_two_examples*lkw5Tu3h_pVXzVUe.

As far as I can tell, the random seed is used only for determining a random starting point within the parameter space. Does that answer your question?

Best,
Stefan

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Hi @JayJay92,

The random seed allows you to configure the node in a way that each execution of the node with the same configuration/input produces the same results. One could say that the randomness is the same for each iteration with the same seed. If you are not using any seed, the results will be (slightly) different in each execution since the Hillclimb and the Random Search algorithms are based on randomness.

So if you want to have reproducible results, setting a seed allows you to achieve this.

Cheers,
Simon

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