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Tunable constrained optimization algorithms #324
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…ly modified by the evaluation function
…selection support
…id and evaluated configurations
…d mixed parameter values
…ategies that require only numerics
…numeric parameter values
… parameter lookup
…able, other improvements
…overall neighbor knowledge with every lookup
…implementation, minor improvements
…t constant after restrictions) from the search space
…it to hyperparameter tuning CLI
…emi) constraint-aware
…ices of the parameter values
…merical values are replaced with their index
…numerical configuration
…he discrete constraint search spaces
…to the parameter indices of the given configuration
…izes where possible for numpy arrays
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This PR combines the hyperparameter tuning with the constrained optimization algorithms, new differential evolution implementation, and support for custom strategies.
In addition, it brings several performance improvements to the handling and lookup of neighbors in the search space: