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License: MIT License
Chimera: hierarchy-based multi-objective optimization
License: MIT License
From a search efficiency perspective, would you recommend using noisy expected hypervolume improvement (NEHVI) with a reference point set based on domain knowledge or Chimera with parameters set based on domain knowledge? Are there any numerical results showing a comparison between Chimera and NEHVI, e.g. via Olympus?
Chimera does not accept arrays of numpy.bool_
as absolutes
argument. An error is raised when checking the input. Example:
Chimera(tolerances=np.array([0.5, 0.2]), absolutes=np.array([False, False]), goals=['min', 'max'])
>>> ValueError: `absolute` should be a list of True/False values
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