v 1.0.0

1.0.0

• Added new Python API to generate reference points with DasDarren1998. The new class
  allows getting the weights for the NSGA3 algorithm and plotting them. See the Python
  type hints for name and description of the new class methods.
• Added AdaptiveNSGA3 to use the adaptive approach to handle the reference points. This
  implements the new algorithm from Jain and Deb (2014) (doi.org/10.1109/TEVC.2013.2281534)
  to handle problems where not all reference points intersect the optimal Pareto front. This
  helps to reduce crowding and enhance the solution quality. See the
  new example file
  and results.
• The algorithm additional data are now exported in AlgorithExport in the Export additional_data in AlgorithmExport
  field. This contains, for example, the reference points for NSGA3.