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Make Bayesian Optimization parallelize more

Currently the implementation of Bayesian Optimization in WMLA is completely sequential. This renders it essentially useless for models with long training times. It would be ideal to parallelize it more.
There are two angles to this: first, at its most basic level, it should be possible to specify starting the method by launching an initial number of trials at random or generated from one type of designed experiment (I believe the latter is how it currently works, it just doesn't execute them in parallel). The implementation of TPE already works in this way, so that it should not be much of a problem. Second, after this initial set of trials rather than proceeding in a fully sequential mode, one could run next not the top most promising point, but the k top most promising points (subject to a crowding constraint to prevent all next points to be picked from the same area -- I would be happy to work with you on the technical details of the selection heuristic if need be).

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