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add support for constrained optimization

• 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

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

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

v4.0.0

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

• 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)

• 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

API-change to improve usage. Class accepts training data. "search"-method accepts search_config and other optimization-run specific arguments like n_iter, n_jobs, optimizer.

• small api-change
• extend progress bar information
• re-enable multiprocessing for new api