v4.8.0

• add support for numpy v2
• add support for pandas v2
• add support for python 3.12
• transfer setup.py to pyproject.toml
• change project structure to src-layout

v4.7.0

• add Genetic algorithm optimizer
• add Differential evolution optimizer

v4.6.0

add support for constrained optimization

v4.5.0

• add early stopping feature to custom optimization strategies
• display additional outputs from objective-function in results in command-line
• add type hints to hyperactive-api
• add tests for new features
• add test for verbosity=False

v4.4.0

• add new feature: "optimization strategies"
• redesign progress-bar

v4.3.0

• add new features from GFO
  • add Spiral Optimization
  • add Lipschitz Optimizer
  • add DIRECT Optimizer
  • print the random seed for reproducibility

v4.0.0

v4.0.0

v3.2.4

Changes from v3.0.0 -> v3.2.4:

• Decouple number of runs from active processes (Thanks to PartiallyTyped). This reduces memory load if number of jobs is huge
• New feature: The progress board enables the user to monitor the optimization progress during the run.
  • Display trend of best score
  • Plot parameters and score in parallel coordinates
  • Generate filter file to define an upper and/or lower bound for all parameters and the score in the parallel coordinate plot
  • List parameters of 5 best scores
• add Python 3.8 to tests
• add warnings of search space values does not contain lists
• improve stability of result-methods
• add tests for hyperactive-memory + search spaces

v2.3.0

• add Tree-structured optimization algorithm (idea from Hyperopt)
• add Decision-tree optimization algorithm (idea from sklearn)
• enable new optimization parameters for bayes-opt:
  • max_sample_size: maximum number of samples for the gaussian-process-reg to train on. Sampling done by random choice.
  • skip_retrain: skips the retraining of the gaussian-process-reg sometimes during the optimization run. Basically returns multiple predictions for next output (which should be apart from another)

v2.1.0

• first stable implementation of "long-term-memory" to save/load search positions/parameter and results.
• enable warm start of sequence based optimizers (bayesian opt, ...) with results from "long-term-memory"
• enable the usage of other gaussian-process-regressors than from sklearn. GPR-class (from gpy, GPflow, ...) can be passed to "optimizer"-kwarg