Feel free to give opinionated answers. What have you chosen to use and why? What has worked well for you, either from personal experience, or from what you’ve seen?
For example, “vanilla BO” is typically
a Gaussian process surrogate model complemented by the expected improvement acquisition function. Meanwhile, in terms of the acquisition functions, there are others such as probability of improvement and upper confidence bound.
Likewise, some may want to favor exploration over exploitation more explicitly.
So far, I have used no-code platform (Atinary) where I used the open-source algorithm Gryffin. Currently, I’m using Ax (thanks to your tutorials!
In the beginning, I tried really hard to get the algorithms from Prof. Alán Aspuru-Guzik’s group running on my system, but found it challenging to even install the package. It would have been great to have some tutorials for those as well! Since I couldn’t get that to work, I’ve stuck with Ax, which I’m still learning and using.
At the moment, I am focusing on fine-tuning the balance between exploration and exploitation within Ax. I am experimenting with different acquisition functions and trying to better understand how to control this aspect of the optimization process.
Since I couldn’t get that to work, I’ve stuck with Ax, which I’m still learning and using.