1. Your second idea was right: "each evaluation is computed on one worker, and 64 evaluations on their specific cores are computed in parallel". So if you have 64 workers and set MaxObjectiveEvaluations=64, you will get 64 function evaluations performed at the same time, each using 1 worker and 1 core, and then the optimization will stop. Those 64 function evaluations will be run on random hyperparameter values, so bayesopt will never fit models or do any intelligent choosing of points.
2. After it says “Copying objective function to workers...Done copying objective function to workers”, your 64 function evaluations begin running in parallel. If it takes hours before you see anything else, then apparently, running a single one of your function evaluations takes hours. The evaluations are run asynchronously, so the next thing you will see are the results of the first one that finishes. Then, as each job finishes, you will see more and more reports, each with gradually increasing runtimes. The reason the subsequent reports appear faster than the first one appeared is that those jobs have been running the whole time, and so are probably nearly done at the time the first job finishes.
To speed things up, I would suggest reducing 'kfold' from 20 to 5.
