Documentation improvements

[ArnoStrouwen]

• Not every optimizer/surrogate/sampler has a clear example what parameters have to be passed for it to work.
  Often the box around a code snippet is missing, and there are missing doc-strings.

http://surrogates.sciml.ai/dev/optimizations/
http://surrogates.sciml.ai/dev/surrogate/
http://surrogates.sciml.ai/dev/samples/

• Stheno is mentioned on the front page as a possible surrogate, but the example of how to use it seems to have been removed.
  I like using Stheno.jl (and GaussianProcesses.jl) as my surrogate as they provide a maximum likelihood method to automatically tune the hyper parameters of the Gaussian process.

• The description on how to define a new surrogate might be incomplete. Don't some optimizers also require the variance, and not only the expectation?

[ludoro]

Thanks for the comments Arno, will look into it in the weekend. As for your point number three, it's true and I should write that more explicitly.

[ArnoStrouwen]

• Could documentation about EGO() and RTEA() be added to http://surrogates.sciml.ai/stable/optimizations/ ?
• It is not fully clear from the docs which optimizers can sample multiple new points at once, i.e. batches of observations, and which ones can only handle one new observation at a time.
• Are there any optimizers that can deal with discrete factors x?

[maxkapur]

The surrogate_optimize() function seems underdocumented as well. Only some of the methods listed in the documentation have docstrings, and it is not clear what I am suppose to pass as the second argument (e.g. ::DYCORS).

I could only find one example in the docs that actually calls surrogate_optimize(), and it passes a function RTEA as the second argument, which is itself undocumented.

[Spinachboul]

RTEA: Real Time Evolutionary Algorithm.
Its one of the optimization techniques used to handle functions with expensive evaluations.

@maxkapur
This feature should be documented in the official along with a brief description of its arguements.