Version 0.11.0

Major changes

• Local search now starts from observed configurations with high acquisition function values, low cost and the from
  unobserved configurations with high acquisition function values found by random search (#509)
• Reduces the number of mandatory requirements (#516)
• Make Gaussian processes more resilient to linalg error by more aggressively adding noise to the diagonal (#511)
• Inactive hyperparameters are now imputed with a value outside of the modeled range (-1) (#508)
• Replace the GP library George by scikit-learn (#505)
• Renames facades to better reflect their use cases (#492), and adds a table to help deciding which facade to use (#495)
• SMAC facades now accept class arguments instead of object arguments (#486)

Minor changes

• Vectorize local search for improved speed (#500)
• Extend the Sobol and LHD initial design to work for non-continuous hyperparameters as well applying an idea similar
  to inverse transform sampling (#494)

Bug fixes

• Fixes a regression in the validation scripts (#519)
• Fixes a unit test regression with numpy 1.17 (#523)
• Fixes an error message (#510)
• Fixes an error making random search behave identical for all seeds