Comments (4)
Can you explain this more in-depth? Sure, it makes no sense to evaluate a point multiple times if the fun is deterministic since it will result in the same outcome. Should we simply check, if the point is already in the optimization path or do I misunderstand the problem?
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No, you understand it correctly. But I am unsure how to handle this correctly. Let us first discuss it before we do anything.
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We do have filterProposedPoints / tol. But that only works for numerical params.
Actually that issue appeared when we only had a bunch of categorical params, for a deterministic test function.
from mlrmbo.
Actually that issue appeared when we only had a bunch of categorical params, for a deterministic > test function.
For this we could simply check whether the porposed point already exists in the design.
Or maybe better: use a metric for mixed spaces.
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Related Issues (20)
- invalid class “km” object: the number of experiments must be larger than the spatial dimension HOT 2
- invalid class “km” object: the number of experiments must be larger than the spatial dimension HOT 7
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- optimizing "multiple objective" functions with "constraints" HOT 3
- Error: unused arguments (forbidden = expression(x2 > x1)) HOT 1
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- Error: Error in as.data.frame.OptPathDF(opt.path, include.rest = FALSE) : No elements where selected (via 'dob' and 'eol')! HOT 8
- Error: Setting of final.method and final.evals for multi-objective optimization not supported at the moment.
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